LMU BioDB 2017 - User contributions [en]

Template:Dbashour

2017-12-16T00:35:24Z

Dbashour: added group report

[[User:dbashour | Dina Bashoura]]

[[Main_Page | Biological Databases Homepage]]

'''List of Assignments'''
*[[Week 1]]
*[[Week 2]]
*[[Week 3]]
*[[Week 4]]
*[[Week 5]]
*[[Week 6]]
*[[Week 7]]
*[[Week 8]]
*[[Week 9]]
*[[Week 10]]
*[[Week 11]]
*[[Week 12]]
*[[Week 14]]
*[[Week 15]]
'''List of Individual Journal Entries'''
*[[Dbashour_week_2 | dbashour Week 2]]
*[[Dbashour_Week_3 | dbashour Week 3]]
*[[Dbashour_week_4 | dbashour Week 4]]
*[[The_Monarch_Initiative | dbashour Week 5]]
*[[Dbashour_Week_6 | dbashour Week 6]]
*[[Dbashour_Week_7 | dbashour Week 7]]
*[[Dbashour_Week_8 | dbashour Week 8]]
*[[Dbashour_Week_9 | dbashour Week 9]]
*[[Dbashour_Week_10 | dbashour Week 10]]
*[[Dbashour_Week_11 | dbashour Week 11]]
*[[Dbashour_Week_12 | dbashour Week 12]]
*[[Dbashour_Week_14 | dbashour Week 14]]
*[[Dbashour_Week_15 | dbashour Week 15]]

'''List of Shared Journal Entries'''
*[[Class_Journal_Week_1#After_Reading_the_Denning_articles_and_Janovy_Chapter | Class Journal Week 1]]
*[[Class_Journal_Week_2 | Class Journal Week 2]]
*[[Class_Journal_Week_3 | Class Journal Week 3]]
*[[Class_Journal_Week_4 | Class Journal Week 4]]
*[[Class_Journal_Week_5 | Class Journal Week 5]]
*[[Class_Journal_Week_6 | Class Journal Week 6]]
*[[Class_Journal_Week_7 | Class Journal Week 7]]
*[[Class_Journal_Week_8 | Class Journal Week 8]]
*[[Class_Journal_Week_9 | Class Journal Week 9]]
*[[Class_Journal_Week_10 | Class Journal Week 10]]

'''List of Final Assignments'''
*[[Dbashour_Final_Reflection | dbashour Final Reflection]]
*[[Gene_hAPI_Deliverables | Gene hAPI Deliverables]]
*[[Media:Gene_hAPI_Group_Report.pdf | Final Group Report]]

'''List of Team Journal Assignments'''
*[[Gene_hAPI | Gene hAPI]]

[[category:Journal Entry]]

Gene hAPI Deliverables

2017-12-16T00:33:55Z

Dbashour: /* Gene hAPI Deliverables */

[[Gene hAPI]]

Members:
*[[User:Cazinge|Eddie Azinge]]
*[[User:Dbashour|Dina Bashoura]]
*[[User:Johnllopez616|John Lopez]]
*[[User:Cwong34|Corinne Wong]]

= Gene hAPI Deliverables =
# Organized team deliverables wiki page
#* [[Gene_hAPI_Deliverables|Gene hAPI Deliverables page (this current page)]]
# Group report
#* [[Media:Gene_hAPI_Group_Report.pdf | Final Group Report]]
# Individual statements
#* Eddie A.: [[Cazinge_Final_Reflection | Eddie's Individual Statement]]
#* Dina: [[Dbashour_Final_Reflection | Dina's Individual Statement]]
#* John: [[Johnllopez616_Individual_Statement|John's Individual Statement]]
#* Corinne: [[Cwong34_Individual_Statement|Corinne's individual statement]]
# Group PowerPoint presentation
#* [[Media:Gene_hAPI_Final_Presentation.pptx | Gene hAPI final presentation]]
#* [[Media:JLopezEAzingeJournalClub.pptx | Coders' Journal Club Presentation]] 
#* [[Media:Cold_Shock_Yeast_Genome_Response.pdf | QA/Data Analyst Journal Club Presentation]]
# Code (GitHub pull request)
#* [https://github.com/bhamilton18/GRNsight Github]
# README
#* [[Media:GetGeneInformationREADME.txt | getGeneInformation() Readme]]
# Excel spreadsheet with ANOVA results/stem formatting
#*[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]]
# PowerPoint of screenshots of stem results
#*[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]]
# Gene List and GO List files from each significant profile
#*[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]]
# YEASTRACT "rank by TF" results
#*[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]]
# GRNmap input workbook (with network adjency matrix)
#*[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]]
# GRNmap output workbook
#*[[Media:15-genes_32-edges_team-hAPI_Sigmoid_estimation_output.xlsx | GRNmap dGLN3 output]]
# Electronic notebooks
#* [[Dbashour_Week_8|Dina's Week 8]]
#* [[Dbashour_Week_10|Dina's Week 10]]
#* [[Dbashour_Week_11|Dina's Week 11]]
#* [[Dbashour_Week_12|Dina's Week 12]]
#* [[Dbashour_Week_14|Dina's Week 14]]
#* [[Dbashour_Week_15|Dina's Week 15]]

= Deliverables Checklist =

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to both Dr. Dahlquist and Dr. Dionisio)
# Group PowerPoint presentation (given on Tuesday, December 12, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Code (GitHub pull request)
#* Each team should coordinate in performing a final integration and integration testing iteration (see [[Coder]] milestone for details) which the Interaction and Integration team then submits to the ''original'' GRNsight GitHub repository as a single, unified pull request from the class project’s fork
# Supply a README that summarizes the functionality of your team's new feature (''.txt'' or ''.md'', ''one README per team'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of screenshots of stem results (''.pptx'')
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'')
# Electronic notebook corresponding to these the microarray results files ([[Week 8]], [[Week 10]], and Weeks 11-15) support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=Group Report Update=
*1-2 page introduction
*Methods
**Data analyst
**QA
**Coders
*Combine results/discussion - add few sentences about significance after each result
**DA
**QA
**Coders
*Conclusion 1-2 pages
**Connect to JC papers
**Future direction
**Summary

[[Category: Gene hAPI]]

[[Category: Group Project]]

[[Category: Deliverables]]

Dbashour Final Reflection

2017-12-16T00:32:24Z

Dbashour: /* Wiki Links */ added group project

=Statement of Work=
For this project, I was assigned the role of data analyst. I began by correcting my [[Dbashour_Week_8 | week 8]] and [[Dbashour_Week_10 | week 10]] assignments for any corrections that needed to be made based on the feedback of Dr. Dahlquist. I worked with the QA/Project Manager Corinne to present an article for the journal club in [[Dbashour_Week_11 | week 11]]. From then on, she worked to manage the team and organize each week while I worked on the data analysis part of my assignment. I spent roughly 2 weeks completing the data analysis on my own, getting corrections from Dr. Dahlquist when needed. Once that was done, I generated a regulatory matrix using GRNsight and GRNmap to visualize the network. For the presentation, I input all of my data files and discussed the results. I did the same for the paper, except expanding in greater detail on the methods, results, and discussion. Along with Corinne, I contacted our group to make sure we were all on the same page, completing the assignments that needed to be completed on time. I made sure to always have my files organized on a flash drive, a lesson that I had to learn, and easily available to anyone in my team who needed it. I also worked with my guild to complete some of the data analysis as well as utilized them to answer any questions that I had regarding the assignments.
*please refer to the following wiki pages for more detailed information:
** [[Dbashour_Week_8 | week 8]]
** [[Dbashour_Week_10 | week 10]]
** [[Dbashour_Week_11 | week 11]]
** [[Dbashour_Week_12 | week 12]]
** [[Dbashour_Week_14 | week 14]]
** [[Dbashour_Week_15 | week 15]]
** [[Gene_hAPI_Deliverables | final deliverables]]

=Assessment of Project=
Our project workflow and teamflow was better than I could have hoped for. We each completed our work in a timely manner and were respectful and profession with eachother. If we ever had trouble with something or needed assistance on a certain task, we could easily go to one another for that help or clarification. The coders worked together and the QA and myself worked together. This grouping worked because we had similar tasks and would then communicate with eachother on what we were doing. Overall, I really enjoyed working with this group and I think we all contributed an equal amount of work. The only trouble we had was on that communication between Coders and QA/Data analyst. This communication was troublesome because as the data analyst I never knew what the coders were doing. I understood their general assignment and goal but I was never sure of what their specific progress was towards that goal. Albeit, this did not stop me from succeeding or hinder our performance as a group. If I could do this project again, I wouldn't change that much. I would increase the communication between coders and analyst and maybe set up our deliverables page and team page earlier so I had a overview of what the goal of the project was early on. I also would change my time management, working on correcting the week 8 and 10 assignment earlier so I did not have so much work all to do in one.
 
I found the quality of our work to be very good. We included all the information we were supposed to for the paper and our gene page. The information was informative and strategically chosen. Because our project manager was very good with organization, all of our wiki pages were set up nicely. Our deliverables page was organized so all we had to do was add in the files under bullets that were already placed for us. Our paper was also set up nicely so that we just included our own individual parts.

=Reflection on the Process=
I learned with my head that computer science is a lot more difficult than I thought. I originally took this class because I always wanted to take a computer science class since I would never have exposure to it otherwise and the best time for me to do so would be in college where I have that opportunity. But I quickly learned that it entails a lot more than I would have thought. Coding is very tedious and time consuming and learning languages takes time and practice. Coding is not for everybody; you need patience and the motivation to accomplish tasks. I learned with my heart that it is important to communicate well when working in groups. I learned that having team members assigned to a specific task is very helpful and the role of the project manager is the most helpful and useful role in organizing a project. I also learned that having guilds is also useful so there is always someone to ask questions when needed, someone who is working on the same task that you are. I learned with my hands that it is important to back up your files to an external drive at all times. I learned how to analyze large sets of data, becoming more familiar with excel functions, and running statistical tests for significance. I also developed the skill of creating a visual network of transcription factors.

=Wiki Links=
[[Gene_hAPI | Gene hAPI Team Page]] 
[[Gene_hAPI_Deliverables | Final Deliverables]] 
[[Media:Gene_hAPI_Final_Presentation.pptx|Gene hAPI Final Presentation]] 
[[Media:Gene_hAPI_Group_Report.pdf | Final Group Report]] 

{{template:dbashour}}

File:DGLN3 ppt Dina.pptx

2017-12-16T00:30:16Z

Dbashour: Dbashour uploaded a new version of File:DGLN3 ppt Dina.pptx

Dbashour Final Reflection

2017-12-16T00:16:33Z

Dbashour: added template and wiki links

=Statement of Work=
For this project, I was assigned the role of data analyst. I began by correcting my [[Dbashour_Week_8 | week 8]] and [[Dbashour_Week_10 | week 10]] assignments for any corrections that needed to be made based on the feedback of Dr. Dahlquist. I worked with the QA/Project Manager Corinne to present an article for the journal club in [[Dbashour_Week_11 | week 11]]. From then on, she worked to manage the team and organize each week while I worked on the data analysis part of my assignment. I spent roughly 2 weeks completing the data analysis on my own, getting corrections from Dr. Dahlquist when needed. Once that was done, I generated a regulatory matrix using GRNsight and GRNmap to visualize the network. For the presentation, I input all of my data files and discussed the results. I did the same for the paper, except expanding in greater detail on the methods, results, and discussion. Along with Corinne, I contacted our group to make sure we were all on the same page, completing the assignments that needed to be completed on time. I made sure to always have my files organized on a flash drive, a lesson that I had to learn, and easily available to anyone in my team who needed it. I also worked with my guild to complete some of the data analysis as well as utilized them to answer any questions that I had regarding the assignments.
*please refer to the following wiki pages for more detailed information:
** [[Dbashour_Week_8 | week 8]]
** [[Dbashour_Week_10 | week 10]]
** [[Dbashour_Week_11 | week 11]]
** [[Dbashour_Week_12 | week 12]]
** [[Dbashour_Week_14 | week 14]]
** [[Dbashour_Week_15 | week 15]]
** [[Gene_hAPI_Deliverables | final deliverables]]

=Assessment of Project=
Our project workflow and teamflow was better than I could have hoped for. We each completed our work in a timely manner and were respectful and profession with eachother. If we ever had trouble with something or needed assistance on a certain task, we could easily go to one another for that help or clarification. The coders worked together and the QA and myself worked together. This grouping worked because we had similar tasks and would then communicate with eachother on what we were doing. Overall, I really enjoyed working with this group and I think we all contributed an equal amount of work. The only trouble we had was on that communication between Coders and QA/Data analyst. This communication was troublesome because as the data analyst I never knew what the coders were doing. I understood their general assignment and goal but I was never sure of what their specific progress was towards that goal. Albeit, this did not stop me from succeeding or hinder our performance as a group. If I could do this project again, I wouldn't change that much. I would increase the communication between coders and analyst and maybe set up our deliverables page and team page earlier so I had a overview of what the goal of the project was early on. I also would change my time management, working on correcting the week 8 and 10 assignment earlier so I did not have so much work all to do in one.
 
I found the quality of our work to be very good. We included all the information we were supposed to for the paper and our gene page. The information was informative and strategically chosen. Because our project manager was very good with organization, all of our wiki pages were set up nicely. Our deliverables page was organized so all we had to do was add in the files under bullets that were already placed for us. Our paper was also set up nicely so that we just included our own individual parts.

=Reflection on the Process=
I learned with my head that computer science is a lot more difficult than I thought. I originally took this class because I always wanted to take a computer science class since I would never have exposure to it otherwise and the best time for me to do so would be in college where I have that opportunity. But I quickly learned that it entails a lot more than I would have thought. Coding is very tedious and time consuming and learning languages takes time and practice. Coding is not for everybody; you need patience and the motivation to accomplish tasks. I learned with my heart that it is important to communicate well when working in groups. I learned that having team members assigned to a specific task is very helpful and the role of the project manager is the most helpful and useful role in organizing a project. I also learned that having guilds is also useful so there is always someone to ask questions when needed, someone who is working on the same task that you are. I learned with my hands that it is important to back up your files to an external drive at all times. I learned how to analyze large sets of data, becoming more familiar with excel functions, and running statistical tests for significance. I also developed the skill of creating a visual network of transcription factors.

=Wiki Links=
[[Gene_hAPI | Gene hAPI Team Page]]
[[Gene_hAPI_Deliverables | Final Deliverables]]
[[Media:Gene_hAPI_Final_Presentation.pptx|Gene hAPI Final Presentation]]
[[Media: ----- | Final Group Report]]

{{template:dbashour}}

Dbashour Final Reflection

2017-12-16T00:11:15Z

Dbashour: added reflection on the process

=Statement of Work=
For this project, I was assigned the role of data analyst. I began by correcting my [[Dbashour_Week_8 | week 8]] and [[Dbashour_Week_10 | week 10]] assignments for any corrections that needed to be made based on the feedback of Dr. Dahlquist. I worked with the QA/Project Manager Corinne to present an article for the journal club in [[Dbashour_Week_11 | week 11]]. From then on, she worked to manage the team and organize each week while I worked on the data analysis part of my assignment. I spent roughly 2 weeks completing the data analysis on my own, getting corrections from Dr. Dahlquist when needed. Once that was done, I generated a regulatory matrix using GRNsight and GRNmap to visualize the network. For the presentation, I input all of my data files and discussed the results. I did the same for the paper, except expanding in greater detail on the methods, results, and discussion. Along with Corinne, I contacted our group to make sure we were all on the same page, completing the assignments that needed to be completed on time. I made sure to always have my files organized on a flash drive, a lesson that I had to learn, and easily available to anyone in my team who needed it. I also worked with my guild to complete some of the data analysis as well as utilized them to answer any questions that I had regarding the assignments.
*please refer to the following wiki pages for more detailed information in my electronic notebook:
** [[Dbashour_Week_8 | week 8]]
** [[Dbashour_Week_10 | week 10]]
** [[Dbashour_Week_11 | week 11]]
** [[Dbashour_Week_12 | week 12]]
** [[Dbashour_Week_14 | week 14]]
** [[Dbashour_Week_15 | week 15]]
** [[Gene_hAPI_Deliverables | final deliverables]]

=Assessment of Project=
Our project workflow and teamflow was better than I could have hoped for. We each completed our work in a timely manner and were respectful and profession with eachother. If we ever had trouble with something or needed assistance on a certain task, we could easily go to one another for that help or clarification. The coders worked together and the QA and myself worked together. This grouping worked because we had similar tasks and would then communicate with eachother on what we were doing. Overall, I really enjoyed working with this group and I think we all contributed an equal amount of work. The only trouble we had was on that communication between Coders and QA/Data analyst. This communication was troublesome because as the data analyst I never knew what the coders were doing. I understood their general assignment and goal but I was never sure of what their specific progress was towards that goal. Albeit, this did not stop me from succeeding or hinder our performance as a group. If I could do this project again, I wouldn't change that much. I would increase the communication between coders and analyst and maybe set up our deliverables page and team page earlier so I had a overview of what the goal of the project was early on. I also would change my time management, working on correcting the week 8 and 10 assignment earlier so I did not have so much work all to do in one.
 
I found the quality of our work to be very good. We included all the information we were supposed to for the paper and our gene page. The information was informative and strategically chosen. Because our project manager was very good with organization, all of our wiki pages were set up nicely. Our deliverables page was organized so all we had to do was add in the files under bullets that were already placed for us. Our paper was also set up nicely so that we just included our own individual parts.

=Reflection on the Process=
I learned with my head that computer science is a lot more difficult than I thought. I originally took this class because I always wanted to take a computer science class since I would never have exposure to it otherwise and the best time for me to do so would be in college where I have that opportunity. But I quickly learned that it entails a lot more than I would have thought. Coding is very tedious and time consuming and learning languages takes time and practice. Coding is not for everybody; you need patience and the motivation to accomplish tasks. I learned with my heart that it is important to communicate well when working in groups. I learned that having team members assigned to a specific task is very helpful and the role of the project manager is the most helpful and useful role in organizing a project. I also learned that having guilds is also useful so there is always someone to ask questions when needed, someone who is working on the same task that you are. I learned with my hands that it is important to back up your files to an external drive at all times. I learned how to analyze large sets of data, becoming more familiar with excel functions, and running statistical tests for significance. I also developed the skill of creating a visual network of transcription factors.

Template:Dbashour

2017-12-15T23:26:01Z

Dbashour: added final reflection and deliverables page

[[User:dbashour | Dina Bashoura]]

[[Main_Page | Biological Databases Homepage]]

'''List of Assignments'''
*[[Week 1]]
*[[Week 2]]
*[[Week 3]]
*[[Week 4]]
*[[Week 5]]
*[[Week 6]]
*[[Week 7]]
*[[Week 8]]
*[[Week 9]]
*[[Week 10]]
*[[Week 11]]
*[[Week 12]]
*[[Week 14]]
*[[Week 15]]
'''List of Individual Journal Entries'''
*[[Dbashour_week_2 | dbashour Week 2]]
*[[Dbashour_Week_3 | dbashour Week 3]]
*[[Dbashour_week_4 | dbashour Week 4]]
*[[The_Monarch_Initiative | dbashour Week 5]]
*[[Dbashour_Week_6 | dbashour Week 6]]
*[[Dbashour_Week_7 | dbashour Week 7]]
*[[Dbashour_Week_8 | dbashour Week 8]]
*[[Dbashour_Week_9 | dbashour Week 9]]
*[[Dbashour_Week_10 | dbashour Week 10]]
*[[Dbashour_Week_11 | dbashour Week 11]]
*[[Dbashour_Week_12 | dbashour Week 12]]
*[[Dbashour_Week_14 | dbashour Week 14]]
*[[Dbashour_Week_15 | dbashour Week 15]]

'''List of Shared Journal Entries'''
*[[Class_Journal_Week_1#After_Reading_the_Denning_articles_and_Janovy_Chapter | Class Journal Week 1]]
*[[Class_Journal_Week_2 | Class Journal Week 2]]
*[[Class_Journal_Week_3 | Class Journal Week 3]]
*[[Class_Journal_Week_4 | Class Journal Week 4]]
*[[Class_Journal_Week_5 | Class Journal Week 5]]
*[[Class_Journal_Week_6 | Class Journal Week 6]]
*[[Class_Journal_Week_7 | Class Journal Week 7]]
*[[Class_Journal_Week_8 | Class Journal Week 8]]
*[[Class_Journal_Week_9 | Class Journal Week 9]]
*[[Class_Journal_Week_10 | Class Journal Week 10]]

'''List of Final Assignments'''
*[[Dbashour_Final_Reflection | dbashour Final Reflection]]
*[[Gene_hAPI_Deliverables | Gene hAPI Deliverables]]

'''List of Team Journal Assignments'''
*[[Gene_hAPI | Gene hAPI]]

[[category:Journal Entry]]

Gene hAPI Deliverables

2017-12-15T23:24:49Z

Dbashour: /* Gene hAPI Deliverables */ added final statement

[[Gene hAPI]]

Members:
*[[User:Cazinge|Eddie Azinge]]
*[[User:Dbashour|Dina Bashoura]]
*[[User:Johnllopez616|John Lopez]]
*[[User:Cwong34|Corinne Wong]]

= Gene hAPI Deliverables =
# Organized team deliverables wiki page
#* [[Gene_hAPI_Deliverables|Gene hAPI Deliverables page (this current page)]]
# Group report
#*
# Individual statements
#* Eddie A.:
#* Dina: [[Dbashour_Final_Reflection | Dina's Individual Statement]]
#* John: [[Johnllopez616_Individual_Statement|John's Individual Statement]]
#* Corinne: [[Cwong34_Individual_Statement|Corinne's individual statement]]
# Group PowerPoint presentation
#* [[Media:Gene_hAPI_Final_Presentation.pptx | Gene hAPI final presentation]]
#* [[Media:JLopezEAzingeJournalClub.pptx | Coders' Journal Club Presentation]] 
#* [[Media:Cold_Shock_Yeast_Genome_Response.pdf | QA/Data Analyst Journal Club Presentation]]
# Code (GitHub pull request)
#*
# README
#* [[Media:GetGeneInformationREADME.txt | getGeneInformation() Readme]]
# Excel spreadsheet with ANOVA results/stem formatting
#*[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]]
# PowerPoint of screenshots of stem results
#*[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]]
# Gene List and GO List files from each significant profile
#*[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]]
# YEASTRACT "rank by TF" results
#*[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]]
# GRNmap input workbook (with network adjency matrix)
#*[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]]
# GRNmap output workbook
#*[[Media:15-genes_32-edges_team-hAPI_Sigmoid_estimation_output.xlsx | GRNmap dGLN3 output]]
# Electronic notebooks
#* [[Dbashour_Week_8|Dina's Week 8]]
#* [[Dbashour_Week_10|Dina's Week 10]]
#* [[Dbashour_Week_11|Dina's Week 11]]
#* [[Dbashour_Week_12|Dina's Week 12]]
#* [[Dbashour_Week_14|Dina's Week 14]]
#* [[Dbashour_Week_15|Dina's Week 15]]

= Deliverables Checklist =

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to both Dr. Dahlquist and Dr. Dionisio)
# Group PowerPoint presentation (given on Tuesday, December 12, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Code (GitHub pull request)
#* Each team should coordinate in performing a final integration and integration testing iteration (see [[Coder]] milestone for details) which the Interaction and Integration team then submits to the ''original'' GRNsight GitHub repository as a single, unified pull request from the class project’s fork
# Supply a README that summarizes the functionality of your team's new feature (''.txt'' or ''.md'', ''one README per team'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of screenshots of stem results (''.pptx'')
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'')
# Electronic notebook corresponding to these the microarray results files ([[Week 8]], [[Week 10]], and Weeks 11-15) support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=Group Report Update=
*1-2 page introduction
*Methods
**Data analyst
**QA
**Coders
*Combine results/discussion - add few sentences about significance after each result
**DA
**QA
**Coders
*Conclusion 1-2 pages
**Connect to JC papers
**Future direction
**Summary

[[Category: Gene hAPI]]

[[Category: Group Project]]

[[Category: Deliverables]]

Dbashour Week 15

2017-12-15T23:21:10Z

Dbashour: updated week 15 with link to deliverables page

= Electronic Notebook =
This week I started by organizing my deliverables from [[Dbashour_Week_14 | Week 14]]. Dr. Dahlquist assisted me in looking over my deliverables from [[Dbashour_Week_14 | Week 14]] and fixing the files that needed to be fixed. After this, she ran the files through MatLab so that I would retrieve an output .xlsx file with images corresponding to the information. Along with this, I met with my team to organize the powerpoint presentation and final paper. We met in person in the Seaver lab and worked to gather all of our deliverables and formulate our overall progress and accomplishments. We worked on completing the final paper in person as well as via text throughout the week until our final deadline.

= Final Project =
Refer to [[Gene hAPI Deliverables | Gene hAPI's Deliverables page]] for a wiki page of our final deliverables including all files and final paper.

=Acknowledgements=
Dr. Dahlquist helped me in running Matlab and correcting the files that needed to be corrected for GRNsight. The data analyst guild assisted me with any final questions I had when working on the final paper.

=References=
# Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
# LMU BioDB 2017. (2017). Week 15. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_15

{{template:dbashour}}

Gene hAPI Deliverables

2017-12-15T23:13:37Z

Dbashour: updated deliverables

[[Gene hAPI]]

Members:
*[[User:Cazinge|Eddie Azinge]]
*[[User:Dbashour|Dina Bashoura]]
*[[User:Johnllopez616|John Lopez]]
*[[User:Cwong34|Corinne Wong]]

= Gene hAPI Deliverables =
# Organized team deliverables wiki page
#* [[Gene_hAPI_Deliverables|Gene hAPI Deliverables page (this current page)]]
# Group report
#*
# Individual statements
#* Eddie A.:
#* Dina:
#* John:
#* Corinne: [[Cwong34_Individual_Statement|Corinne's individual statement]]
# Group PowerPoint presentation
#* [[Media:Gene_hAPI_Final_Presentation.pptx | Gene hAPI final presentation]]
#* [[Media:JLopezEAzingeJournalClub.pptx | Coders' Journal Club Presentation]] 
#* [[Media:Cold_Shock_Yeast_Genome_Response.pdf | QA/Data Analyst Journal Club Presentation]]
# Code (GitHub pull request)
#*
# README
#* [[Media:GetGeneInformationREADME.txt | getGeneInformation() Readme]]
# Excel spreadsheet with ANOVA results/stem formatting
#*[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]]
# PowerPoint of screenshots of stem results
#*[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]]
# Gene List and GO List files from each significant profile
#*[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]]
# YEASTRACT "rank by TF" results
#*[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]]
# GRNmap input workbook (with network adjency matrix)
#*[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]]
# GRNmap output workbook
#*[[Media:15-genes_32-edges_team-hAPI_Sigmoid_estimation_output.xlsx | GRNmap dGLN3 output]]
# Electronic notebooks
#* [[Dbashour_Week_8|Dina's Week 8]]
#* [[Dbashour_Week_10|Dina's Week 10]]
#* [[Dbashour_Week_11|Dina's Week 11]]
#* [[Dbashour_Week_12|Dina's Week 12]]
#* [[Dbashour_Week_14|Dina's Week 14]]
#* [[Dbashour_Week_15|Dina's Week 15]]

= Deliverables Checklist =

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to both Dr. Dahlquist and Dr. Dionisio)
# Group PowerPoint presentation (given on Tuesday, December 12, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Code (GitHub pull request)
#* Each team should coordinate in performing a final integration and integration testing iteration (see [[Coder]] milestone for details) which the Interaction and Integration team then submits to the ''original'' GRNsight GitHub repository as a single, unified pull request from the class project’s fork
# Supply a README that summarizes the functionality of your team's new feature (''.txt'' or ''.md'', ''one README per team'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of screenshots of stem results (''.pptx'')
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'')
# Electronic notebook corresponding to these the microarray results files ([[Week 8]], [[Week 10]], and Weeks 11-15) support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=Group Report Update=
*1-2 page introduction
*Methods
**Data analyst
**QA
**Coders
*Combine results/discussion - add few sentences about significance after each result
**DA
**QA
**Coders
*Conclusion 1-2 pages
**Connect to JC papers
**Future direction
**Summary

[[Category: Gene hAPI]]

[[Category: Group Project]]

[[Category: Deliverables]]

File:15-genes 32-edges team-hAPI Sigmoid estimation output.xlsx

2017-12-15T23:12:37Z

Dbashour:

Gene hAPI

2017-12-15T23:02:46Z

Dbashour: updated week 15

==Gene hAPI==

===Links===
Our Deliverables: [[Gene hAPI Deliverables]]

Main Page: [[Main Page]]

Project Page: [[GRNsight Gene Page Project]]

[http://dondi.github.io/GRNsight/ GRNsight]

{{Template:GRNsight Gene Page Project Links}}

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
* [[Cazinge Week 12|Week 12]]:
** This week my task was to set up my development environment for the coder milestones. As such, I've completed milestones 1-3, talked to the other coders, and established a plan to work on the assignment in the future along with the rest of my team.
** Setting up milestones 1-3 went pretty well; since they were all standard open source project tasks, I was able to complete them simply, efficiently, and without any real friction.
** The only thing that I found less than desirable from this week was our communication; As Thanksgiving weekend rolls around we only stand to fall behind if we continue without amending our communication situation.
** This next week, I'll focus on keeping an open line of communication with the rest of my team, as well as completing the majority of the coding milestones that we have left.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
*[[Cazinge Week 14|Week 14]]:
**This week's we reached a good spot in terms of the execution of Milestone 4. I was the main point of contact with regards to general coding questions, and we're approaching the completion of the final assignment.
**This week was fun as I was able to leisurely interact with my classmates in an environment more typically representing that of actual software development. As such, I was able to help out with typical software development problems and speed up the development of the final project.
**I don't feel as if I struggled with any specific part of this week's assignment, but we haven't exactly been proactive about writing our tests, so that eventually needs to be resolved.
**As far as what needs to get done for this next week, testing. Other than that, I'm feeling good about our progress going into the last week.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)
*[[Cazinge Week 15|Week 15]]:
**This week we finished the final functionality of the API function, marking our progress with the final project complete.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)

'''Dina''':
* [[Dbashour_Week_11 | Week 11]]
** For this week, I assisted with creating our group wiki page, adding the template and setting up the page in preparation for the later weeks to come. I also located two out of 4 research articles regarding cold shock, yeast, and microarray data that we might later use for our final project. I became familiar with how to narrow down search queries in order to yield the smallest amount of results that are related to your actual topic, as well as identify how many articles are cited and how many articles cite the articles I found.
** I liked being able to work on the assignment in class, this way I was able to ask questions or clarify certain things that I needed help with. I also like that we have a guild of people, this way there is more of a support system if I am ever lost or need assistance.
** Because my task for this week was simply following the directions on the wiki, there was nothing that went wrong or needed fixing.
** Next week, I will work with my guild to present on our found articles and collaborate with them all in order to make our presentation run smoothly. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:43, 13 November 2017 (PST)
*[[Dbashour_Week_12 | Week 12]]
** For this week, Corrine and I prepared for the journal club presentation by individually reading the article "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. After reading this article, I developed an outline that highlighted the main points in each section of the article. With Corrine, I made a presentation on that outline, making sure to include each highlighted point. I also located and defined 10 words in the article that I was unfamiliar with and presented a flowchart of the overall experimental design.
** What worked well for this week was to collaborate with Corrine on who was going to present which parts of the article, this way there was no confusion on that matter and the overall flow of the presentation would be well executed.
** There was a lack of communication with all of us as a team, so I am unsure as to what John and Eddie have done for this week.
** Next week, we will all communicate via text either with the entire class or with just my group in order to clarify what the role of each person is and if we have been completing our tasks. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 12:10, 21 November 2017 (PST)
*[[Dbashour_Week_14 | Week 14]]
** This week, I corrected my week 8 and week 10 assignments on my [[Dbashour_Week_14 | Week 14]] individual page., fixing anything that Dr. Dahlquist and Dr. Dionisio had requested to fix on my talk page. I made my electronic notebook be written in the past tense, corrected my files, and added any information I needed to add. Also this week I completed the remainder of the week 10 assignment that we had not complete during that week. I made a network model of transcription factors based on my deletion strain dGLN3. I also met with the data analysts to see if we were all on the same page and made a group message in order to have a method of communication for any questions we had.
** Being on top of my assignments and my time management during class worked well this week. I was able to ask questions and accomplish a lot from working on my assignment in class in a timely manner.
** I would have liked to meet more with my group and touch base on where we all resided in the project. I wish that we updated each other more on what we were working on, specifically between the coders and the analysts/QA.
** Next week, I hope to continue my efforts of time management in class and communicate more with the coders to see how far we have all come. I will specifically ask the coders what they have done and what they plan on doing next in order to facilitate the communication barrier. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)
*[[Dbashour_Week_15 | Week 15]]
** This week, Dr. Dahlquist reviewed my files and made corrections to them so that they would work and be visualized in GRNsight. I organized my final deliverables in order to prepare for the final project. As a team we worked together to complete the final powerpoint and paper, meeting in class as well as outside of class to work on it. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)

'''Corinne''':
* [[Cwong34_Week_11|Week 11]]:
** This week I helped to create the template and group page. I found two sources to contribute to our annotated bibliography, which we can use to research information for our final project. I researched the accessibility and publishing details of the sources.
** It was nice that we had some time in class to work, so we could easily check in with each other about what we were working on. It was also nice that Dina and I were working on the same project, so we could easily understand our work and help each other.
** It was a bit difficult to be fully aware of the progress of everyone because we had separate projects for this week and next week too. Both halves of our group are working on separate presentations, so we have different focuses, which makes it harder to keep up with each other.
** Next week, since it will be a similar situation, I will try to keep up with the other half of my group by fully understanding their objectives/assignments for the week and checking in some more.
*[[Cwong34_Week_12|Week 12]]:
**This week, I read the "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. I came up with a flow chart of their methods for the experiment and made an outline for my individual assignment. I also found ten terms I didn't know and wrote down their definitions in my individual journal. I worked with Dina on our presentation of the article over the weekend.
**Having a collaborative assignment worked for me and Dina because we knew exactly what we needed to do for the project this week, and we worked together to get it done. Moreover, we had more time to meet up with each other this weekend, so it was easier to collaborate.
**It was still difficult to keep in touch with all of the members of the group since we all had different things to work on.
**However, now that we are done with our separate presentations, it will be more of a focus to know what each person is working on. Moreover, I will work to create more communication between group members to keep track of our progress, whether it's on our gene page or over text.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 23:26, 20 November 2017 (PST)

*[[Cwong34_Week_14|Week 14]]:
**This week, I met with the group members to check in on where we were in the project. I met with the other QAs, and we worked together to come up with a list of information to pull from databases, and specifically which information to pull from where.
**Having time to work on the project in class was very helpful to be able to meet with everyone. I could easily communicate to my team members and check in with them, but I could also check in with other teams to work together. There was a lot more communication this week, and I feel I have a good idea of our progression for the rest of the semester.
**Having to split the class time between working with our team members and working with our guilds was a bit of a challenge because there was a lot to cover between both groups. We made it work, but hopefully we'll be able to find a time to meet outside of class as well this coming week to work on our project.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 00:25, 5 December 2017 (PST)

*[[Cwong34_Week_15|Week 15]]
**

'''John''':
* [[Johnllopez Week 11|Week 11]]:
** This week my task was mostly focused around organizing the Journal Club Presentation. I created the initial outline for the powerpoint, created the style, and added over half the content to it. In addition, I spent some time with [[User:cazinge | Eddie Azinge]] who finished most of the deliverable portion of the assignment as he explained to me his process.
** I felt the assembling of the group was done so with ease because I worked with each of the people in the past on different assignments/projects. I felt like each of my members are dedicated and willing to work, so I'm glad that we have a good group. Eddie's experience in coding was an advantage for me because I shouldn't have any sort of confusion, however as I explain below, it is also a disadvantage.
** Despite the fact that Eddie provided an explanation for how he was able to develop the primary deliverable for the project, I felt a little disappointed that I couldn't go through the same discovery process he did. It essentially makes me feel like my role in the project isn't as important. In addition, I was also irritated that we didn't have many opportunities to work on the Journal Club Presentations in advance, for I felt like we weren't as prepared to create/give them as we could have been.
**Next week, I know that I have to get a head start on my individual portion of the assignment so that I'm not crunched for time like I was for the presentation. In addition, it's imperative that I review the code Eddie set up in detail so I can understand each line and how it works. Furthermore, I have to follow along with the other coders in setting up development rigs.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:41, 13 November 2017 (PST)

* [[Johnllopez Week 12|Week 12]]:
** This week my task involved setting up my development environment for the later milestones. It included the establishment of milestones 1-3, talking to the other coders, and establishing a plan to work on the assignment in the future.
** I felt like the setting up of the environment went well. Once I understood what I had to do, it was not difficult to set up the environment. In addition, I had no problems setting it up on GitHub and my computer once it happened.
** Unfortunately I felt like this week wasn't a very productive week. Despite setting up the environment, I did not collaborate well with my teammates. We have yet to arrange a proper work schedule and plan, especially to ensure that over the thanksgiving break we are able to do some stuff. This has led to the group's progress existing in a state of limbo.
** On Thursday, I asked the class to obtain a WhatsApp to allow for communication. This will be essential to communicate between the coders to accomplish the project. On Tuesday I will connect with as many people in the class. More importantly, I will get my group together to work on a set plan to work on the assignment and establish deadlines. Although this week wasn't the most productive for me, I will ensure that we don't fall behind next week.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:27, 20 November 2017 (PST)

*[[johnllopez Week 14|Week 14]]:
**This week's primary objective was to get as much of Milestone 4 as finished as possible. My portion of the assignment was to learn how to extract data from XML files and translate it so that the page developers could use it.
**Perhaps the best part about this portion of the assignment was being able to discover a bit of jQuery and DOM functions. Knowledge and exposure to both of these is fundamental in obtaining a potential job.
**One area where I struggled was figuring out how to extract JSON using javascript. This is something that both of the Eddies, however, were proficient in doing, so it's important that I seek their help in understanding this concept. In addition, I wish I communicated more with the coders during the development of the project.
**This week will be crucial in finishing Milestone 4 for the project. It's absolutely necessary that the coders and I not only integrate everything, but that I am following along with what they do. I will ask a lot of questions to truly understand what's happening.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:16, 4 December 2017 (PST)

==Journal Club Deliverable==
[[Media:JLopezEAzingeJournalClub.pptx | The presentation - Coder/Designer]] 
[[Media:Cold_Shock_Yeast_Genome_Response.pdf | Journal Club Week 12 Presentation - QA/Data Analyst]]

== Journal Club Article ==
Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.
 
{{template:Gene_hAPI}}

=Acknowledgments=
#We received help and guidance from Dr. Dondi and Dr. Dahlquist.

=References=
#Becerra M., Lombardía L.J., González-Siso M.I., Rodríguez-Belmonte E., Hauser N.C., & Cerdán M.E. (2003). Genome-wide analysis of the yeast transcriptome upon heat and cold shock. Comparative and Functional Genomics, 4(4), 366-375. doi: 10.1002/cfg.301
#Paul Ford, 2015, Bloomberg, Retrieved from: https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/#the-time-you-attended-the-e-mail-address-validation-meeting
#Homma T., Iwahashi H., & Komatsu Y. (2003). Yeast gene expression during growth at low temperature. Cryobiology, 46(3), 230-237. https://doi.org/10.1016/S0011-2240(03)00028-2
#LMU BioDB 2017. (2017). GRNsight Gene Page Project. Retrieved from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/GRNsight_Gene_Page_Project
#Murata et. al.(2006). Genome-wide expression analysis of yeast response during exposure to 4 C. Extremophiles, 10(2), 117-128.
#Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.

Category:Gene hAPI

2017-12-14T02:24:21Z

Dbashour: category created

Team: Gene hAPI category

Gene hAPI Deliverables

2017-12-12T07:42:43Z

Dbashour: /* Our Deliverables */ added final presentation

== Deliverables Checklist ==

# Organized Team deliverables wiki page (or other media (CD or flash drive) with table of contents)
# Group Report (''.doc'', ''.docx'' or ''.pdf'' file)
# Individual statements of work, assessments, reflections (wiki page, ''.doc'', ''.docx'', ''.pdf'', or e-mailed to both Dr. Dahlquist and Dr. Dionisio)
# Group PowerPoint presentation (given on Tuesday, December 12, ''.ppt'', ''.pptx'' or ''.pdf'' file)
# Code (GitHub pull request)
#* Each team should coordinate in performing a final integration and integration testing iteration (see [[Coder]] milestone for details) which the Interaction and Integration team then submits to the ''original'' GRNsight GitHub repository as a single, unified pull request from the class project’s fork
# Supply a README that summarizes the functionality of your team's new feature (''.txt'' or ''.md'', ''one README per team'')
# Excel spreadsheet with ANOVA results/stem formatting (''.xlsx'')
# PowerPoint of screenshots of stem results (''.pptx'')
# Gene List and GO List files from each significant profile (''.txt'' compressed together in a ''.zip'' file)
# YEASTRACT "rank by TF" results (''.xlsx'')
# GRNmap input workbook (with network adjacency matrix, ''.xlsx'')
# GRNmap output workbook (''.xlsx'')
# Electronic notebook corresponding to these the microarray results files ([[Week 8]], [[Week 10]], and Weeks 11-15) support ''reproducible research'' so that all manipulations of the data and files are documented so that someone else could begin with your starting file, follow the protocol, and obtain your results.

=Group Report Update=
*1-2 page introduction
*Methods
**Data analyst
**QA
**Coders
*Combine results/discussion - add few sentences about significance after each result
**DA
**QA
**Coders
*Conclusion 1-2 pages
**Connect to JC papers
**Future direction
**Summary

== Our Deliverables ==
#[[Media:JLopezEAzingeJournalClub.pptx | The presentation - Coder/Designer]] 
#[[Media:Cold_Shock_Yeast_Genome_Response.pdf | Journal Club Week 12 Presentation - QA/Data Analyst]]
#[[Media:Gene_hAPI_Final_Presentation.pptx | Gene hAPI final presentation]]

File:Gene hAPI Final Presentation.pptx

2017-12-12T07:41:18Z

Dbashour: final

final

Gene hAPI

2017-12-12T03:31:23Z

Dbashour: updated week 15

==Gene hAPI==

===Links===
Our Deliverables: [[Gene hAPI Deliverables]]

Main Page: [[Main Page]]

Project Page: [[GRNsight Gene Page Project]]

[http://dondi.github.io/GRNsight/ GRNsight]

{{Template:GRNsight Gene Page Project Links}}

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
* [[Cazinge Week 12|Week 12]]:
** This week my task was to set up my development environment for the coder milestones. As such, I've completed milestones 1-3, talked to the other coders, and established a plan to work on the assignment in the future along with the rest of my team.
** Setting up milestones 1-3 went pretty well; since they were all standard open source project tasks, I was able to complete them simply, efficiently, and without any real friction.
** The only thing that I found less than desirable from this week was our communication; As Thanksgiving weekend rolls around we only stand to fall behind if we continue without amending our communication situation.
** This next week, I'll focus on keeping an open line of communication with the rest of my team, as well as completing the majority of the coding milestones that we have left.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
*[[Cazinge Week 14|Week 14]]:
**This week's we reached a good spot in terms of the execution of Milestone 4. I was the main point of contact with regards to general coding questions, and we're approaching the completion of the final assignment.
**This week was fun as I was able to leisurely interact with my classmates in an environment more typically representing that of actual software development. As such, I was able to help out with typical software development problems and speed up the development of the final project.
**I don't feel as if I struggled with any specific part of this week's assignment, but we haven't exactly been proactive about writing our tests, so that eventually needs to be resolved.
**As far as what needs to get done for this next week, testing. Other than that, I'm feeling good about our progress going into the last week.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)
*[[Cazinge Week 15|Week 15]]:
**This week we finished the final functionality of the API function, marking our progress with the final project complete.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)

'''Dina''':
* [[Dbashour_Week_11 | Week 11]]
** For this week, I assisted with creating our group wiki page, adding the template and setting up the page in preparation for the later weeks to come. I also located two out of 4 research articles regarding cold shock, yeast, and microarray data that we might later use for our final project. I became familiar with how to narrow down search queries in order to yield the smallest amount of results that are related to your actual topic, as well as identify how many articles are cited and how many articles cite the articles I found.
** I liked being able to work on the assignment in class, this way I was able to ask questions or clarify certain things that I needed help with. I also like that we have a guild of people, this way there is more of a support system if I am ever lost or need assistance.
** Because my task for this week was simply following the directions on the wiki, there was nothing that went wrong or needed fixing.
** Next week, I will work with my guild to present on our found articles and collaborate with them all in order to make our presentation run smoothly. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:43, 13 November 2017 (PST)
*[[Dbashour_Week_12 | Week 12]]
** For this week, Corrine and I prepared for the journal club presentation by individually reading the article "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. After reading this article, I developed an outline that highlighted the main points in each section of the article. With Corrine, I made a presentation on that outline, making sure to include each highlighted point. I also located and defined 10 words in the article that I was unfamiliar with and presented a flowchart of the overall experimental design.
** What worked well for this week was to collaborate with Corrine on who was going to present which parts of the article, this way there was no confusion on that matter and the overall flow of the presentation would be well executed.
** There was a lack of communication with all of us as a team, so I am unsure as to what John and Eddie have done for this week.
** Next week, we will all communicate via text either with the entire class or with just my group in order to clarify what the role of each person is and if we have been completing our tasks. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 12:10, 21 November 2017 (PST)
*[[Dbashour_Week_14 | Week 14]]
** This week, I corrected my week 8 and week 10 assignments on my [[Dbashour_Week_14 | Week 14]] individual page., fixing anything that Dr. Dahlquist and Dr. Dionisio had requested to fix on my talk page. I made my electronic notebook be written in the past tense, corrected my files, and added any information I needed to add. Also this week I completed the remainder of the week 10 assignment that we had not complete during that week. I made a network model of transcription factors based on my deletion strain dGLN3. I also met with the data analysts to see if we were all on the same page and made a group message in order to have a method of communication for any questions we had.
** Being on top of my assignments and my time management during class worked well this week. I was able to ask questions and accomplish a lot from working on my assignment in class in a timely manner.
** I would have liked to meet more with my group and touch base on where we all resided in the project. I wish that we updated each other more on what we were working on, specifically between the coders and the analysts/QA.
** Next week, I hope to continue my efforts of time management in class and communicate more with the coders to see how far we have all come. I will specifically ask the coders what they have done and what they plan on doing next in order to facilitate the communication barrier. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)
*[[Dbashour_Week_15 | Week 15]]
** This week, Dr. Dahlquist reviewed my files and made corrections to them so that they would work and be visualized in GRNsight. I organized my final deliverables in order to prepare for the final project. As a team we worked together to complete the final powerpoint. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)

'''Corinne''':
* [[Cwong34_Week_11|Week 11]]:
** This week I helped to create the template and group page. I found two sources to contribute to our annotated bibliography, which we can use to research information for our final project. I researched the accessibility and publishing details of the sources.
** It was nice that we had some time in class to work, so we could easily check in with each other about what we were working on. It was also nice that Dina and I were working on the same project, so we could easily understand our work and help each other.
** It was a bit difficult to be fully aware of the progress of everyone because we had separate projects for this week and next week too. Both halves of our group are working on separate presentations, so we have different focuses, which makes it harder to keep up with each other.
** Next week, since it will be a similar situation, I will try to keep up with the other half of my group by fully understanding their objectives/assignments for the week and checking in some more.
*[[Cwong34_Week_12|Week 12]]:
**This week, I read the "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. I came up with a flow chart of their methods for the experiment and made an outline for my individual assignment. I also found ten terms I didn't know and wrote down their definitions in my individual journal. I worked with Dina on our presentation of the article over the weekend.
**Having a collaborative assignment worked for me and Dina because we knew exactly what we needed to do for the project this week, and we worked together to get it done. Moreover, we had more time to meet up with each other this weekend, so it was easier to collaborate.
**It was still difficult to keep in touch with all of the members of the group since we all had different things to work on.
**However, now that we are done with our separate presentations, it will be more of a focus to know what each person is working on. Moreover, I will work to create more communication between group members to keep track of our progress, whether it's on our gene page or over text.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 23:26, 20 November 2017 (PST)

*[[Cwong34_Week_14|Week 14]]:
**This week, I met with the group members to check in on where we were in the project. I met with the other QAs, and we worked together to come up with a list of information to pull from databases, and specifically which information to pull from where.
**Having time to work on the project in class was very helpful to be able to meet with everyone. I could easily communicate to my team members and check in with them, but I could also check in with other teams to work together. There was a lot more communication this week, and I feel I have a good idea of our progression for the rest of the semester.
**Having to split the class time between working with our team members and working with our guilds was a bit of a challenge because there was a lot to cover between both groups. We made it work, but hopefully we'll be able to find a time to meet outside of class as well this coming week to work on our project.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 00:25, 5 December 2017 (PST)

*[[Cwong34_Week_15|Week 15]]
**

'''John''':
* [[Johnllopez Week 11|Week 11]]:
** This week my task was mostly focused around organizing the Journal Club Presentation. I created the initial outline for the powerpoint, created the style, and added over half the content to it. In addition, I spent some time with [[User:cazinge | Eddie Azinge]] who finished most of the deliverable portion of the assignment as he explained to me his process.
** I felt the assembling of the group was done so with ease because I worked with each of the people in the past on different assignments/projects. I felt like each of my members are dedicated and willing to work, so I'm glad that we have a good group. Eddie's experience in coding was an advantage for me because I shouldn't have any sort of confusion, however as I explain below, it is also a disadvantage.
** Despite the fact that Eddie provided an explanation for how he was able to develop the primary deliverable for the project, I felt a little disappointed that I couldn't go through the same discovery process he did. It essentially makes me feel like my role in the project isn't as important. In addition, I was also irritated that we didn't have many opportunities to work on the Journal Club Presentations in advance, for I felt like we weren't as prepared to create/give them as we could have been.
**Next week, I know that I have to get a head start on my individual portion of the assignment so that I'm not crunched for time like I was for the presentation. In addition, it's imperative that I review the code Eddie set up in detail so I can understand each line and how it works. Furthermore, I have to follow along with the other coders in setting up development rigs.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:41, 13 November 2017 (PST)

* [[Johnllopez Week 12|Week 12]]:
** This week my task involved setting up my development environment for the later milestones. It included the establishment of milestones 1-3, talking to the other coders, and establishing a plan to work on the assignment in the future.
** I felt like the setting up of the environment went well. Once I understood what I had to do, it was not difficult to set up the environment. In addition, I had no problems setting it up on GitHub and my computer once it happened.
** Unfortunately I felt like this week wasn't a very productive week. Despite setting up the environment, I did not collaborate well with my teammates. We have yet to arrange a proper work schedule and plan, especially to ensure that over the thanksgiving break we are able to do some stuff. This has led to the group's progress existing in a state of limbo.
** On Thursday, I asked the class to obtain a WhatsApp to allow for communication. This will be essential to communicate between the coders to accomplish the project. On Tuesday I will connect with as many people in the class. More importantly, I will get my group together to work on a set plan to work on the assignment and establish deadlines. Although this week wasn't the most productive for me, I will ensure that we don't fall behind next week.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:27, 20 November 2017 (PST)

*[[johnllopez Week 14|Week 14]]:
**This week's primary objective was to get as much of Milestone 4 as finished as possible. My portion of the assignment was to learn how to extract data from XML files and translate it so that the page developers could use it.
**Perhaps the best part about this portion of the assignment was being able to discover a bit of jQuery and DOM functions. Knowledge and exposure to both of these is fundamental in obtaining a potential job.
**One area where I struggled was figuring out how to extract JSON using javascript. This is something that both of the Eddies, however, were proficient in doing, so it's important that I seek their help in understanding this concept. In addition, I wish I communicated more with the coders during the development of the project.
**This week will be crucial in finishing Milestone 4 for the project. It's absolutely necessary that the coders and I not only integrate everything, but that I am following along with what they do. I will ask a lot of questions to truly understand what's happening.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:16, 4 December 2017 (PST)

==Journal Club Deliverable==
[[Media:JLopezEAzingeJournalClub.pptx | The presentation - Coder/Designer]] 
[[Media:Cold_Shock_Yeast_Genome_Response.pdf | Journal Club Week 12 Presentation - QA/Data Analyst]]

== Journal Club Article ==
Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.
 
{{template:Gene_hAPI}}

=Acknowledgments=
#We received help and guidance from Dr. Dondi and Dr. Dahlquist.

=References=
#Becerra M., Lombardía L.J., González-Siso M.I., Rodríguez-Belmonte E., Hauser N.C., & Cerdán M.E. (2003). Genome-wide analysis of the yeast transcriptome upon heat and cold shock. Comparative and Functional Genomics, 4(4), 366-375. doi: 10.1002/cfg.301
#Paul Ford, 2015, Bloomberg, Retrieved from: https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/#the-time-you-attended-the-e-mail-address-validation-meeting
#Homma T., Iwahashi H., & Komatsu Y. (2003). Yeast gene expression during growth at low temperature. Cryobiology, 46(3), 230-237. https://doi.org/10.1016/S0011-2240(03)00028-2
#LMU BioDB 2017. (2017). GRNsight Gene Page Project. Retrieved from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/GRNsight_Gene_Page_Project
#Murata et. al.(2006). Genome-wide expression analysis of yeast response during exposure to 4 C. Extremophiles, 10(2), 117-128.
#Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.

Template:Dbashour

2017-12-10T00:17:15Z

Dbashour: updated for week 14 and 15

[[User:dbashour | Dina Bashoura]]

[[Main_Page | Biological Databases Homepage]]

'''List of Assignments'''
*[[Week 1]]
*[[Week 2]]
*[[Week 3]]
*[[Week 4]]
*[[Week 5]]
*[[Week 6]]
*[[Week 7]]
*[[Week 8]]
*[[Week 9]]
*[[Week 10]]
*[[Week 11]]
*[[Week 12]]
*[[Week 14]]
*[[Week 15]]
'''List of Individual Journal Entries'''
*[[Dbashour_week_2 | dbashour Week 2]]
*[[Dbashour_Week_3 | dbashour Week 3]]
*[[Dbashour_week_4 | dbashour Week 4]]
*[[The_Monarch_Initiative | dbashour Week 5]]
*[[Dbashour_Week_6 | dbashour Week 6]]
*[[Dbashour_Week_7 | dbashour Week 7]]
*[[Dbashour_Week_8 | dbashour Week 8]]
*[[Dbashour_Week_9 | dbashour Week 9]]
*[[Dbashour_Week_10 | dbashour Week 10]]
*[[Dbashour_Week_11 | dbashour Week 11]]
*[[Dbashour_Week_12 | dbashour Week 12]]
*[[Dbashour_Week_14 | dbashour Week 14]]
*[[Dbashour_Week_15 | dbashour Week 15]]
'''List of Shared Journal Entries'''
*[[Class_Journal_Week_1#After_Reading_the_Denning_articles_and_Janovy_Chapter | Class Journal Week 1]]
*[[Class_Journal_Week_2 | Class Journal Week 2]]
*[[Class_Journal_Week_3 | Class Journal Week 3]]
*[[Class_Journal_Week_4 | Class Journal Week 4]]
*[[Class_Journal_Week_5 | Class Journal Week 5]]
*[[Class_Journal_Week_6 | Class Journal Week 6]]
*[[Class_Journal_Week_7 | Class Journal Week 7]]
*[[Class_Journal_Week_8 | Class Journal Week 8]]
*[[Class_Journal_Week_9 | Class Journal Week 9]]
*[[Class_Journal_Week_10 | Class Journal Week 10]]
[[category:Journal Entry]]
'''List of Team Journal Assignments'''
*[[Gene_hAPI | Gene hAPI]]

Dbashour Week 14

2017-12-10T00:15:26Z

Dbashour:

=Electronic Notebook=
==Week 8 Corrections==
* put electronic notebook in the past tense.
* switched the Bonferroni and B-H p value numbers
* recorded number of replicates, number of NA replaced, and strain
* updated summary paragraph
* incorporated interpretation of p values of NSR1 and ADH1

==Week 10 Corrections==
* put electronic notebook in the past tense.
* added template
* added summary paragraph
* added stem worksheet
* made name of powerpoint more specific with initials and strain name
* added GO term references

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose 15 of the significant transcription factors on my list, adding HAP4 since it was not already on the list and CIN5. I chose these transcription factors among the rest because they are all significant and I kept in mind their % in user set, % in yeastract, and p value. All 15 TFs are listed below.
#**ACE2
#**CIN5
#**GLN3
#**HAP4
#**MSN2
#**PDR1
#**PDR3
#**SFP1
#**SWI5
#**UME6
#**YAP1
#**YHP1
#**YLR278C
#**YOX1
#**ZAP1

#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

=Summary=
This week, I continued the methods for the [[Week 10]] assignment, with the goal of retrieving a network model that can be put into GRNsight and visualized. I created both a black and white version as well as a colored version, with the help of Dr. Dahlquist, that shows the magnitude and direction of the expression in the network. I did this by choosing profile 45 to interpret and analyze and found a list of transcription factors associated with this profile through the website [http://www.yeastract.com/ YEASTRACT database]. This list is what I used for the network. Once the .xlsx file was ready and completed, I inputted it into GRNsight and received a network model in black and white. Then Dr. Dahlquist assisted in fixing my files to be formatted in a way that will make it usable in GRNsight in color as opposed to black and white. These input and output files are located in the deliverables of this wiki and screenshots of the network can be found in the powerpoint of this wiki.

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.
{{template:dbashour}}

Dbashour Week 10

2017-12-10T00:14:10Z

Dbashour: added references

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647
'''The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in yeast and its relation to cold shock.

==Summary==
For this week, we utilized stem to analyze the response of yeast genes to cold shock. We found the GO list and gene list for each profile. From the multiple profiles we received from stem, I selected profile #9 to interpret further because of it's significance and number of genes associated with this profile. We then found genes and GO terms which were most significant in expression changes during cold shock. I chose 6 of these GO terms and defined them and how they related to cold shock's effect on yeast. Yeast will choose to either down regulate or up regulate a gene's expression based on what the genes effect is and how much energy yeast decides to expend on that gene process when cold shocked.

==Deliverable==
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
==Acknowledgements==
*Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the [[Week 10]] wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my [[Dbashour_Week_8 |Week 8]] individual assignment to complete this week's assignment.
'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 15:09, 6 November 2017 (PST)

==References==
#LMU BioDB 2017. (2017). Week 8. Retrieved October 27, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved October 27, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Consortium, G. O. (n.d.). Small molecule metabolic process. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0044281
#Consortium, G. O. (n.d.). Organic acid catabolic process. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0016054
#Consortium, G. O. (n.d.). Hydrolase activity. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0016787
#Consortium, G. O. (n.d.). Coenzyme binding. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0050662
#Consortium, G. O. (n.d.). Cellular response to stress. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0033554
#Consortium, G. O. (n.d.). Protein modification by small protein conjugation or removal. Retrieved December 09, 2017, from http://amigo.geneontology.org/amigo/term/GO:0070647

Dbashour Week 10

2017-12-10T00:04:02Z

Dbashour: /* References */ updated

File:DGLN3 ppt Dina.pptx

2017-12-09T23:58:56Z

Dbashour: Dbashour uploaded a new version of File:DGLN3 ppt Dina.pptx

Dbashour Week 14

2017-12-09T23:48:08Z

Dbashour: added template

=Electronic Notebook=
==Week 8 Corrections==
* put electronic notebook in the past tense.
* switched the Bonferroni and B-H p value numbers
* recorded number of replicates, number of NA replaced, and strain
* updated summary paragraph
* incorporated interpretation of p values of NSR1 and ADH1

==Week 10 Corrections==
* put electronic notebook in the past tense.
* added template
* added summary paragraph
* added stem worksheet
* made name of powerpoint more specific with initials and strain name

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose 15 of the significant transcription factors on my list, adding HAP4 since it was not already on the list and CIN5. I chose these transcription factors among the rest because they are all significant and I kept in mind their % in user set, % in yeastract, and p value. All 15 TFs are listed below.
#**ACE2
#**CIN5
#**GLN3
#**HAP4
#**MSN2
#**PDR1
#**PDR3
#**SFP1
#**SWI5
#**UME6
#**YAP1
#**YHP1
#**YLR278C
#**YOX1
#**ZAP1

#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

=Summary=
This week, I continued the methods for the [[Week 10]] assignment, with the goal of retrieving a network model that can be put into GRNsight and visualized. I created both a black and white version as well as a colored version, with the help of Dr. Dahlquist, that shows the magnitude and direction of the expression in the network. I did this by choosing profile 45 to interpret and analyze and found a list of transcription factors associated with this profile through the website [http://www.yeastract.com/ YEASTRACT database]. This list is what I used for the network. Once the .xlsx file was ready and completed, I inputted it into GRNsight and received a network model in black and white. Then Dr. Dahlquist assisted in fixing my files to be formatted in a way that will make it usable in GRNsight in color as opposed to black and white. These input and output files are located in the deliverables of this wiki and screenshots of the network can be found in the powerpoint of this wiki.

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.
{{template:dbashour}}

Dbashour Week 14

2017-12-09T23:43:32Z

Dbashour: /* Week 10 Continued */ updated TF list

=Electronic Notebook=
==Week 8 Corrections==
* put electronic notebook in the past tense.
* switched the Bonferroni and B-H p value numbers
* recorded number of replicates, number of NA replaced, and strain

==Week 10 Corrections==
* put electronic notebook in the past tense.
*

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose 15 of the significant transcription factors on my list, adding HAP4 since it was not already on the list and CIN5. I chose these transcription factors among the rest because they are all significant and I kept in mind their % in user set, % in yeastract, and p value. All 15 TFs are listed below.
#**ACE2
#**CIN5
#**GLN3
#**HAP4
#**MSN2
#**PDR1
#**PDR3
#**SFP1
#**SWI5
#**UME6
#**YAP1
#**YHP1
#**YLR278C
#**YOX1
#**ZAP1

#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

=Summary=
This week, I continued the methods for the [[Week 10]] assignment, with the goal of retrieving a network model that can be put into GRNsight and visualized. I created both a black and white version as well as a colored version, with the help of Dr. Dahlquist, that shows the magnitude and direction of the expression in the network. I did this by choosing profile 45 to interpret and analyze and found a list of transcription factors associated with this profile through the website [http://www.yeastract.com/ YEASTRACT database]. This list is what I used for the network. Once the .xlsx file was ready and completed, I inputted it into GRNsight and received a network model in black and white. Then Dr. Dahlquist assisted in fixing my files to be formatted in a way that will make it usable in GRNsight in color as opposed to black and white. These input and output files are located in the deliverables of this wiki and screenshots of the network can be found in the powerpoint of this wiki.

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.

Dbashour Week 14

2017-12-09T23:31:23Z

Dbashour: added summary

=Electronic Notebook=
==Week 8 Corrections==
* put electronic notebook in the past tense.
* switched the Bonferroni and B-H p value numbers
* recorded number of replicates, number of NA replaced, and strain

==Week 10 Corrections==
* put electronic notebook in the past tense.
*

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

=Summary=
This week, I continued the methods for the [[Week 10]] assignment, with the goal of retrieving a network model that can be put into GRNsight and visualized. I created both a black and white version as well as a colored version, with the help of Dr. Dahlquist, that shows the magnitude and direction of the expression in the network. I did this by choosing profile 45 to interpret and analyze and found a list of transcription factors associated with this profile through the website [http://www.yeastract.com/ YEASTRACT database]. This list is what I used for the network. Once the .xlsx file was ready and completed, I inputted it into GRNsight and received a network model in black and white. Then Dr. Dahlquist assisted in fixing my files to be formatted in a way that will make it usable in GRNsight in color as opposed to black and white. These input and output files are located in the deliverables of this wiki and screenshots of the network can be found in the powerpoint of this wiki.

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.

Dbashour Week 10

2017-12-09T23:17:09Z

Dbashour: added summary

==Electronic Notebook==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647
'''The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in yeast and its relation to cold shock.

==Summary==
For this week, we utilized stem to analyze the response of yeast genes to cold shock. We found the GO list and gene list for each profile. From the multiple profiles we received from stem, I selected profile #9 to interpret further because of it's significance and number of genes associated with this profile. We then found genes and GO terms which were most significant in expression changes during cold shock. I chose 6 of these GO terms and defined them and how they related to cold shock's effect on yeast. Yeast will choose to either down regulate or up regulate a gene's expression based on what the genes effect is and how much energy yeast decides to expend on that gene process when cold shocked.

==Deliverable==
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
==Acknowledgements==
*Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the [[Week 10]] wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my [[Dbashour_Week_8 |Week 8]] individual assignment to complete this week's assignment.
'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 15:09, 6 November 2017 (PST)

==References==
*http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

Dbashour Week 8

2017-12-09T22:52:38Z

Dbashour: added deliverable

= Electronic Notebook =

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock. NSR1 has an unadjusted p value < 0.05, but is no longer significant with the corrections. This means that we have some confidence that is is really changing in this experiment, but not as much as some other genes.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. ADH1 p values are < 0.05, so the Average Log Fold Changes you see are likely just noise.

===Summary===
Our strain was dGLN3 and we analyzed the data for timepoints 15, 30, 60, 90, and 120. We used that information to calculate the p-value of an ANOVA, the Bonferroni corrected p-value, and the Benjamini and Hochber corrected p-value for each timepoint. We learned how to use excel to facilitate with large datasets like this one. We learned shortcuts to highlighting information like double clicking in the corner of the box as well as dragging the box down to multiple cells in order to copy the function to those respective cells. We concluded by answering the questions related to our findings as well as analyzing the information from the "our favorite gene" assignment. In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression. Specifically, we found that 1185 genes or 19.15% of the data in relation to the wildtype strain had a Benjamini & Hochberg-corrected p value of less than 0.05 and 45 genes or 0.73% of the data in relation to the wildtype strain had a Bonferroni-corrected p value of less than 0.05. Lastly, NSR1 has small p values indicating that it is significant and affected by the cold shock. ADH1, or our favorite gene, was most likely not affected by cold shock because of the p values being above 0.05, meaning that the average log fold changes we see are likely just noise.

=Deliverable=
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 

==Acknowledgements==
I worked with [[user:Zvanysse | Zack]] to complete this week's assignment. We worked in class in order to ask questions as we worked but completed the rest of the assignment separately over the weekend. We consulted via text if we had any questions regarding the assignment. I also copied and modified the [[Week 8]] assignment page and used the data from Dr. Dahlquist's research on yeast and their effect on cold shock.

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''

[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:29, 23 October 2017 (PDT)

==References==
LMU BioDB 2017. (2017). Week 8. Retrieved October 23, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

{{Template:dbashour}}

Dbashour Week 8

2017-12-09T22:48:35Z

Dbashour: added summary

= Electronic Notebook =

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock. NSR1 has an unadjusted p value < 0.05, but is no longer significant with the corrections. This means that we have some confidence that is is really changing in this experiment, but not as much as some other genes.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. ADH1 p values are < 0.05, so the Average Log Fold Changes you see are likely just noise.

===Summary===
Our strain was dGLN3 and we analyzed the data for timepoints 15, 30, 60, 90, and 120. We used that information to calculate the p-value of an ANOVA, the Bonferroni corrected p-value, and the Benjamini and Hochber corrected p-value for each timepoint. We learned how to use excel to facilitate with large datasets like this one. We learned shortcuts to highlighting information like double clicking in the corner of the box as well as dragging the box down to multiple cells in order to copy the function to those respective cells. We concluded by answering the questions related to our findings as well as analyzing the information from the "our favorite gene" assignment. In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression. Specifically, we found that 1185 genes or 19.15% of the data in relation to the wildtype strain had a Benjamini & Hochberg-corrected p value of less than 0.05 and 45 genes or 0.73% of the data in relation to the wildtype strain had a Bonferroni-corrected p value of less than 0.05. Lastly, NSR1 has small p values indicating that it is significant and affected by the cold shock. ADH1, or our favorite gene, was most likely not affected by cold shock because of the p values being above 0.05, meaning that the average log fold changes we see are likely just noise.

==Acknowledgements==
I worked with [[user:Zvanysse | Zack]] to complete this week's assignment. We worked in class in order to ask questions as we worked but completed the rest of the assignment separately over the weekend. We consulted via text if we had any questions regarding the assignment. I also copied and modified the [[Week 8]] assignment page and used the data from Dr. Dahlquist's research on yeast and their effect on cold shock.

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.'''

[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:29, 23 October 2017 (PDT)

==References==
LMU BioDB 2017. (2017). Week 8. Retrieved October 23, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

{{Template:dbashour}}

Dbashour Week 10

2017-12-09T22:30:36Z

Dbashour: /* Acknowledgements */

=='''Homework Assignment'''==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647
'''The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in the cell and its relation to cold shock.
==Deliverable==
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
==Acknowledgements==
*Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the [[Week 10]] wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my [[Dbashour_Week_8 |Week 8]] individual assignment to complete this week's assignment.
'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 15:09, 6 November 2017 (PST)

==References==
*http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

Dbashour Week 10

2017-12-09T22:17:58Z

Dbashour: added deliverables

=='''Homework Assignment'''==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647
'''The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in the cell and its relation to cold shock.
==Deliverable==
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
==Acknowledgements==
*Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the [[Week 10]] wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my [Dbashour_Week_8 |Week 8]] individual assignment to complete this week's assignment.
'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 15:09, 6 November 2017 (PST)
==References==
*http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

Dbashour Week 10

2017-12-09T22:11:51Z

Dbashour: updated corrections

=='''Homework Assignment'''==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647
'''The cell reacts to cold shock by changing expression of genes associated with these go terms in order to survive under stressful conditions. When the cell undergoes stress (i.e. cold shock), it undergoes a change of expression of genes that would be effected by this stressful condition. Specifically in this case, we are looking at if GLN3 was deleted, which is a transcription factor, what effects would it have on the cell. Once this is determined, we would be able to decide the function of GLN3 in the cell and its relation to cold shock.
==Acknowledgements==
*Zack helped me with performing this assignment and analyzing the data. Dr. Dahlquist provided the data through her research on yeast and cold shock. I referred to the [[Week 10]] wiki page to complete this assignment and modified the instructions as well. I also used the deliverable from my [Dbashour_Week_8 |Week 8]] individual assignment to complete this week's assignment.
'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 15:09, 6 November 2017 (PST)
==References==
*http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
*https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

Dbashour Week 14

2017-12-09T21:54:05Z

Dbashour: /* Electronic Notebook */ removed week 8 and 9 methods corrections and put them in corresponding weeks

=Electronic Notebook=
==Week 8 Corrections==
* put electronic notebook in the past tense.
* switched the Bonferroni and B-H p value numbers
* recorded number of replicates, number of NA replaced, and strain

==Week 10 Corrections==
* put electronic notebook in the past tense.
*

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions including: [[User:Mbalducc|Mary Balducci]], [[User:dbashour|Dina Bashoura]], and [[User:Emmatyrnauer|Emma Tyrnauer]].

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.

Dbashour Week 15

2017-12-09T21:35:03Z

Dbashour: /* Electronic Notebook */

= Electronic Notebook =
This week I started by organizing my deliverables from [[Dbashour_Week_14 | Week 14]]. Dr. Dahlquist assisted me in looking over my deliverables from [[Dbashour_Week_14 | Week 14]] and fixing the files that needed to be fixed. After this, she ran the files through MatLab so that I would retrieve an output .xlsx file with images corresponding to the information.

Along with this, I met with my team to organize the powerpoint presentation and final paper. We met in person in the Seaver lab and worked to gather all of our deliverables and formulate our overall progress and accomplishments.

=Acknowledgements=
Dr. Dahlquist helped me in running Matlab and correcting the files that needed to be corrected for GRNsight.

=References=
# Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
# LMU BioDB 2017. (2017). Week 15. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_15

{{template:dbashour}}

Dbashour Week 15

2017-12-09T21:33:33Z

Dbashour: added electronic notebook

Dbashour Week 14

2017-12-09T21:20:26Z

Dbashour: added signature and acknowledgements and references

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

===Summary===
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

= Acknowledgements =
# Recieved help from both [[User:Dondi|Dondi]] and [[User:Kdahlquist|Dr. Dahlquist]] from feedback given and instructions given in class.
# Copied and modified the instructions from [[Week 8]] and [[Week 10|Week 10]] as well as retrieved deliverable file from [[Week 8]]
# Texted the data analyst group chat for questions including: [[User:Mbalducc|Mary Balducci]], [[User:dbashour|Dina Bashoura]], and [[User:Emmatyrnauer|Emma Tyrnauer]].

'''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''' 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:20, 9 December 2017 (PST)
= References =
#Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85.
#LMU BioDB 2017. (2017). Week 14. Retrieved November 28, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_14
#LMU BioDB 2017. (2017). Week 8. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
#LMU BioDB 2017. (2017). Week 10. Retrieved November 29, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
#Teixeira, M. C., Monteiro, P. T., Guerreiro, J. F., Gonçalves, J. P., Mira, N. P., dos Santos, S. C., ... & Madeira, S. C. (2013). The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic acids research, 42(D1), D161-D166.

Gene hAPI

2017-12-09T21:00:39Z

Dbashour: dina added week 14 and 15

==Gene hAPI==

===General Information===
Eddie (Cazinge):
*Role: Coder
*User Page: [[User:cazinge|Eddie Azinge]]

Dina (Dbashour):
*Role: Data Analysis
*User Page: [[User:dbashour|Dina Bashoura]]

Corinne (Cwong34):
*Role: Project Manager/Quality Assurance
*User Page: [[User:Cwong34|Corinne Wong]]

John (johnllopez616):
*Role: Coder
*User Page: [[User:johnllopez616|John Lopez]]

===Executive Summaries===
'''Eddie''':
* [[Cazinge Week 11|Week 11]]:
** This week, I finished the bulk of the function logic for our final project; I also performed research on the items necessary for an effective journal club presentation this coming Thursday by completing the individual assignment that we were assigned for the week.
** Getting a large portion of our designated assignment completed early worked pretty successfully to alleviate our stress over the impending assignment and place us ahead of the pace of other groups so that we would be able to work on the more secondary parts of our final presentation across the next 4 weeks.
** Working on the project without having a proper development environment set up ended up detracting from the fluidity of our collaboration as a team; as the versions of the final function that John and I worked on weren't linked via git, and were somewhat unruly to collaborate on.
** Next time, I'll make sure to set up my development environment according to Dondi's instructions on the Coders Guild Page.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
* [[Cazinge Week 12|Week 12]]:
** This week my task was to set up my development environment for the coder milestones. As such, I've completed milestones 1-3, talked to the other coders, and established a plan to work on the assignment in the future along with the rest of my team.
** Setting up milestones 1-3 went pretty well; since they were all standard open source project tasks, I was able to complete them simply, efficiently, and without any real friction.
** The only thing that I found less than desirable from this week was our communication; As Thanksgiving weekend rolls around we only stand to fall behind if we continue without amending our communication situation.
** This next week, I'll focus on keeping an open line of communication with the rest of my team, as well as completing the majority of the coding milestones that we have left.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 23:39, 20 November 2017 (PST)
*[[Cazinge Week 14|Week 14]]:
**This week's we reached a good spot in terms of the execution of Milestone 4. I was the main point of contact with regards to general coding questions, and we're approaching the completion of the final assignment.
**This week was fun as I was able to leisurely interact with my classmates in an environment more typically representing that of actual software development. As such, I was able to help out with typical software development problems and speed up the development of the final project.
**I don't feel as if I struggled with any specific part of this week's assignment, but we haven't exactly been proactive about writing our tests, so that eventually needs to be resolved.
**As far as what needs to get done for this next week, testing. Other than that, I'm feeling good about our progress going into the last week.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)
*[[Cazinge Week 15|Week 15]]:
**This week we finished the final functionality of the API function, marking our progress with the final project complete.
[[User:Cazinge|Cazinge]] ([[User talk:Cazinge|talk]]) 13:18, 8 December 2017 (PST)

'''Dina''':
* [[Dbashour_Week_11 | Week 11]]
** For this week, I assisted with creating our group wiki page, adding the template and setting up the page in preparation for the later weeks to come. I also located two out of 4 research articles regarding cold shock, yeast, and microarray data that we might later use for our final project. I became familiar with how to narrow down search queries in order to yield the smallest amount of results that are related to your actual topic, as well as identify how many articles are cited and how many articles cite the articles I found.
** I liked being able to work on the assignment in class, this way I was able to ask questions or clarify certain things that I needed help with. I also like that we have a guild of people, this way there is more of a support system if I am ever lost or need assistance.
** Because my task for this week was simply following the directions on the wiki, there was nothing that went wrong or needed fixing.
** Next week, I will work with my guild to present on our found articles and collaborate with them all in order to make our presentation run smoothly. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 23:43, 13 November 2017 (PST)
*[[Dbashour_Week_12 | Week 12]]
** For this week, Corrine and I prepared for the journal club presentation by individually reading the article "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. After reading this article, I developed an outline that highlighted the main points in each section of the article. With Corrine, I made a presentation on that outline, making sure to include each highlighted point. I also located and defined 10 words in the article that I was unfamiliar with and presented a flowchart of the overall experimental design.
** What worked well for this week was to collaborate with Corrine on who was going to present which parts of the article, this way there was no confusion on that matter and the overall flow of the presentation would be well executed.
** There was a lack of communication with all of us as a team, so I am unsure as to what John and Eddie have done for this week.
** Next week, we will all communicate via text either with the entire class or with just my group in order to clarify what the role of each person is and if we have been completing our tasks. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 12:10, 21 November 2017 (PST)
*[[Dbashour_Week_14 | Week 14]]
** This week, I corrected my week 8 and week 10 assignments on my [[Dbashour_Week_14 | Week 14]] individual page., fixing anything that Dr. Dahlquist and Dr. Dionisio had requested to fix on my talk page. I made my electronic notebook be written in the past tense, corrected my files, and added any information I needed to add. Also this week I completed the remainder of the week 10 assignment that we had not complete during that week. I made a network model of transcription factors based on my deletion strain dGLN3. I also met with the data analysts to see if we were all on the same page and made a group message in order to have a method of communication for any questions we had.
** Being on top of my assignments and my time management during class worked well this week. I was able to ask questions and accomplish a lot from working on my assignment in class in a timely manner.
** I would have liked to meet more with my group and touch base on where we all resided in the project. I wish that we updated each other more on what we were working on, specifically between the coders and the analysts/QA.
** Next week, I hope to continue my efforts of time management in class and communicate more with the coders to see how far we have all come. I will specifically ask the coders what they have done and what they plan on doing next in order to facilitate the communication barrier. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)
*[[Dbashour_Week_15 | Week 15]]
** This week, Dr. Dahlquist reviewed my files and made corrections to them so that they would work and be visualized in GRNsight. I organized my final deliverables in order to prepare for the final project. 
[[User:Dbashour|Dbashour]] ([[User talk:Dbashour|talk]]) 13:00, 9 December 2017 (PST)

'''Corinne''':
* [[Cwong34_Week_11|Week 11]]:
** This week I helped to create the template and group page. I found two sources to contribute to our annotated bibliography, which we can use to research information for our final project. I researched the accessibility and publishing details of the sources.
** It was nice that we had some time in class to work, so we could easily check in with each other about what we were working on. It was also nice that Dina and I were working on the same project, so we could easily understand our work and help each other.
** It was a bit difficult to be fully aware of the progress of everyone because we had separate projects for this week and next week too. Both halves of our group are working on separate presentations, so we have different focuses, which makes it harder to keep up with each other.
** Next week, since it will be a similar situation, I will try to keep up with the other half of my group by fully understanding their objectives/assignments for the week and checking in some more.
*[[Cwong34_Week_12|Week 12]]:
**This week, I read the "Comprehensive expression analysis of time-dependent responses in yeast cells to low temperature" by Sahara, T., Goda, T., & Ohgiya, S. I came up with a flow chart of their methods for the experiment and made an outline for my individual assignment. I also found ten terms I didn't know and wrote down their definitions in my individual journal. I worked with Dina on our presentation of the article over the weekend.
**Having a collaborative assignment worked for me and Dina because we knew exactly what we needed to do for the project this week, and we worked together to get it done. Moreover, we had more time to meet up with each other this weekend, so it was easier to collaborate.
**It was still difficult to keep in touch with all of the members of the group since we all had different things to work on.
**However, now that we are done with our separate presentations, it will be more of a focus to know what each person is working on. Moreover, I will work to create more communication between group members to keep track of our progress, whether it's on our gene page or over text.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 23:26, 20 November 2017 (PST)

*[[Cwong34_Week_14|Week 14]]:
**This week, I met with the group members to check in on where we were in the project. I met with the other QAs, and we worked together to come up with a list of information to pull from databases, and specifically which information to pull from where.
**Having time to work on the project in class was very helpful to be able to meet with everyone. I could easily communicate to my team members and check in with them, but I could also check in with other teams to work together. There was a lot more communication this week, and I feel I have a good idea of our progression for the rest of the semester.
**Having to split the class time between working with our team members and working with our guilds was a bit of a challenge because there was a lot to cover between both groups. We made it work, but hopefully we'll be able to find a time to meet outside of class as well this coming week to work on our project.
[[User:Cwong34|Cwong34]] ([[User talk:Cwong34|talk]]) 00:25, 5 December 2017 (PST)

'''John''':
* [[Johnllopez Week 11|Week 11]]:
** This week my task was mostly focused around organizing the Journal Club Presentation. I created the initial outline for the powerpoint, created the style, and added over half the content to it. In addition, I spent some time with [[User:cazinge | Eddie Azinge]] who finished most of the deliverable portion of the assignment as he explained to me his process.
** I felt the assembling of the group was done so with ease because I worked with each of the people in the past on different assignments/projects. I felt like each of my members are dedicated and willing to work, so I'm glad that we have a good group. Eddie's experience in coding was an advantage for me because I shouldn't have any sort of confusion, however as I explain below, it is also a disadvantage.
** Despite the fact that Eddie provided an explanation for how he was able to develop the primary deliverable for the project, I felt a little disappointed that I couldn't go through the same discovery process he did. It essentially makes me feel like my role in the project isn't as important. In addition, I was also irritated that we didn't have many opportunities to work on the Journal Club Presentations in advance, for I felt like we weren't as prepared to create/give them as we could have been.
**Next week, I know that I have to get a head start on my individual portion of the assignment so that I'm not crunched for time like I was for the presentation. In addition, it's imperative that I review the code Eddie set up in detail so I can understand each line and how it works. Furthermore, I have to follow along with the other coders in setting up development rigs.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:41, 13 November 2017 (PST)

* [[Johnllopez Week 12|Week 12]]:
** This week my task involved setting up my development environment for the later milestones. It included the establishment of milestones 1-3, talking to the other coders, and establishing a plan to work on the assignment in the future.
** I felt like the setting up of the environment went well. Once I understood what I had to do, it was not difficult to set up the environment. In addition, I had no problems setting it up on GitHub and my computer once it happened.
** Unfortunately I felt like this week wasn't a very productive week. Despite setting up the environment, I did not collaborate well with my teammates. We have yet to arrange a proper work schedule and plan, especially to ensure that over the thanksgiving break we are able to do some stuff. This has led to the group's progress existing in a state of limbo.
** On Thursday, I asked the class to obtain a WhatsApp to allow for communication. This will be essential to communicate between the coders to accomplish the project. On Tuesday I will connect with as many people in the class. More importantly, I will get my group together to work on a set plan to work on the assignment and establish deadlines. Although this week wasn't the most productive for me, I will ensure that we don't fall behind next week.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:27, 20 November 2017 (PST)

*[[johnllopez Week 14|Week 14]]:
**This week's primary objective was to get as much of Milestone 4 as finished as possible. My portion of the assignment was to learn how to extract data from XML files and translate it so that the page developers could use it.
**Perhaps the best part about this portion of the assignment was being able to discover a bit of jQuery and DOM functions. Knowledge and exposure to both of these is fundamental in obtaining a potential job.
**One area where I struggled was figuring out how to extract JSON using javascript. This is something that both of the Eddies, however, were proficient in doing, so it's important that I seek their help in understanding this concept. In addition, I wish I communicated more with the coders during the development of the project.
**This week will be crucial in finishing Milestone 4 for the project. It's absolutely necessary that the coders and I not only integrate everything, but that I am following along with what they do. I will ask a lot of questions to truly understand what's happening.
[[User:Johnllopez616|Johnllopez616]] ([[User talk:Johnllopez616|talk]]) 23:16, 4 December 2017 (PST)

==Journal Club Deliverable==
[[Media:JLopezEAzingeJournalClub.pptx | The presentation - Coder/Designer]] 
[[Media:Cold_Shock_Yeast_Genome_Response.pdf | Journal Club Week 12 Presentation - QA/Data Analyst]]

== Journal Club Article ==
Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.
 
{{template:Gene_hAPI}}

=Acknowledgments=

=References=
#Becerra M., Lombardía L.J., González-Siso M.I., Rodríguez-Belmonte E., Hauser N.C., & Cerdán M.E. (2003). Genome-wide analysis of the yeast transcriptome upon heat and cold shock. Comparative and Functional Genomics, 4(4), 366-375. doi: 10.1002/cfg.301
#Paul Ford, 2015, Bloomberg, Retrieved from: https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/#the-time-you-attended-the-e-mail-address-validation-meeting
#Homma T., Iwahashi H., & Komatsu Y. (2003). Yeast gene expression during growth at low temperature. Cryobiology, 46(3), 230-237. https://doi.org/10.1016/S0011-2240(03)00028-2
#LMU
#Murata et. al.(2006). Genome-wide expression analysis of yeast response during exposure to 4 C. Extremophiles, 10(2), 117-128.
#Sahara, T., Goda, T., & Ohgiya, S. (2002). Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. Journal of Biological Chemistry, 277(51), 50015-50021.

{{Template:GRNsight Gene Page Project Links}}

File:DGLN3 ppt Dina.pptx

2017-12-08T06:45:01Z

Dbashour: Dbashour uploaded a new version of File:DGLN3 ppt Dina.pptx

Dbashour Week 14

2017-12-08T06:41:41Z

Dbashour: /* Deliverables */ updated

Dbashour Week 14

2017-12-08T06:40:48Z

Dbashour: /* Deliverables */ added stem results powerpoint

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

===Summary===
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

I will analyze the regulatory matrix files I generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First I need to properly format the output files from YEASTRACT. I will repeat these steps for each of the three files I generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, I selected the entire Column A. Then went to the "Data" tab and selected "Text to columns". In the Wizard that appears, I selected "Delimited" and clicked "Next". In the next window, I selected "Semicolon", and clicked "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* I saved this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** I only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), I needed to transpose the matrix. I inserted a new worksheet into my Excel file and named it "network". I went back to the previous sheet and selected the entire matrix and copied it. I went to you new worksheet and clicked on the A1 cell in the upper left. I selected "Paste special" from the "Home" tab. In the window that appears, I checked the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, I deleted the "p" from each of the gene names in the columns. I adjusted the case of the labels to make them all upper case.
#* In cell A1, I copied and pasted the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, I wanted to alphabatize the gene labels both across the top and side.
#** I selected the area of the entire adjacency matrix.
#** I clicked the Data tab and clicked the custom sort button.
#** I sorted Column A alphabetically, being sure to exclude the header row.
#** Then I sorted row 1 from left to right, excluding cell A1. In the Custom Sort window, I clicked on the options button and selected sort left to right, excluding column 1.
#* I named the worksheet containing my organized adjacency matrix "network" and saved it.
# Now I visualized what these gene regulatory networks look like with the GRNsight software.
#* I went to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* I selected the menu item File > Open and selected the regulation matrix .xlsx file that has the "network" worksheet in it that I formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. I moved the nodes (genes) around until I got a layout that I liked and took a screenshot of the results and pasted it into my powerpoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Stem_Results_dGLN3_DB.pptx | dGLN3 stem results DB]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

File:Stem Results dGLN3 DB.pptx

2017-12-08T06:39:47Z

Dbashour:

File:DGLN3 ppt Dina.pptx

2017-12-08T06:38:35Z

Dbashour: Dbashour uploaded a new version of File:DGLN3 ppt Dina.pptx

Dbashour Week 14

2017-12-08T06:27:47Z

Dbashour: /* Visualizing Your Gene Regulatory Networks with GRNsight */ updated

Dbashour Week 14

2017-12-08T06:10:24Z

Dbashour: syntax

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

===Summary===
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###* small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

Dbashour Week 14

2017-12-08T06:08:28Z

Dbashour: /* Week 10 Corrections */ updated

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

===Summary===
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* I downloaded my Excel workbook that you I used for my [[Week 8]] assignment.
#* I Inserted a new worksheet into my Excel workbook, and name it "dGLN3_stem".
#* Select all of the data from your "dGLN3_ANOVA" worksheet and Paste special > paste values into my "dGLN3_stem" worksheet.
#** my leftmost column should have the column header "Master_Index". I renamed this column to "SPOT". Column B is named "ID". I renamed this column to "Gene Symbol". I delete the column named "Standard_Name".
#** I filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, I selected all of the rows (except for my header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I undid the filter. This ensures that I will cluster only the genes with a "significant" change in expression and not the noise. '''''There were 1258 genes left after filtering.'''''
#** I deleted all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, dGLN3_AvgLogFC_t15, etc.).
#** I renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
#** I saved my work. Then used ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Then I download and extracted the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* I clicked on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, I clicked on the ''Browse...'' button to navigate to and select my file.
##* I clicked on the radio button ''No normalization/add 0''.
##* I checked the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, I selected ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. I Selected ''No cross references'', from the Cross Reference Source drop-down menu. I selected ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", I clicked on the radio button that says "Based on real time". Then close the Interface Options window.
##* I took a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## I clicked on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* I took a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_genelist.txt", where I replaced the number symbol with the actual profile number.
##** I upload these files to the wiki and link to them on my individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, I clicked on the "Save Table" button and saved the file to your desktop. I made my filename descriptive of the contents, e.g. "dGLN3_profile#_GOlist.txt". to indicate the dataset and where I replaced the number symbol with the actual profile number. At this point I have saved all of the primary data from the STEM software and it's time to interpret the results!
##** I upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
#* '''''I selected profile #9 to interpret further. I chose this because this profile had a downward pattern, indicating that gene expression decreased with cold shock treatment.'''''
#* '''''118.0 genes belong to this profile.'''''
#* '''''34.1 genes were expected to belong to this profile'''''
#* '''''The p-value for the enrichment of genes in this profile is 1.3E-30 which shows that this expression profile is significantly more than what was expected.'''''
##* I opened the GO list file I saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. I Selected the third row and then chose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''There are 55 GO terms are associated with this profile when a p value < 0.05''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''There are 4 GO terms are associated with this profile when a corrected p value < 0.05.'''''
##* I Selected 6 Gene Ontology terms from my filtered list (either p < 0.05 or corrected p < 0.05).
##** To easily look up the definitions, I went to [http://geneontology.org http://geneontology.org].
##** I copied and pasted the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, I click on the button that says "Link to detailed information about <term>, in this case "biological phase"". Here are the results:
###**small molecule metabolic process - The chemical reactions and pathways involving small molecules, any low molecular weight, monomeric, non-encoded molecule 
###**http://amigo.geneontology.org/amigo/term/GO:0044281
###* organic acid catabolic process - The chemical reactions and pathways resulting in the breakdown of organic acids, any acidic compound containing carbon in covalent linkage 
###**http://amigo.geneontology.org/amigo/term/GO:0016054
###*hydrolase activity - Catalysis of the hydrolysis of various bonds, e.g. C-O, C-N, C-C, phosphoric anhydride bonds, etc. Hydrolase is the systematic name for any enzyme of EC class 3 
###**http://amigo.geneontology.org/amigo/term/GO:0016787
###*co-enzyme binding - Interacting selectively and non-covalently with a coenzyme, any of various nonprotein organic cofactors that are required, in addition to an enzyme and a substrate, for an enzymatic reaction to proceed 
###**http://amigo.geneontology.org/amigo/term/GO:0050662
###*cellular response to stress - Any process that results in a change in state or activity of a cell (in terms of movement, secretion, enzyme production, gene expression, etc.) as a result of a stimulus indicating the organism is under stress. The stress is usually, but not necessarily, exogenous (e.g. temperature, humidity, ionizing radiation) 
###**http://amigo.geneontology.org/amigo/term/GO:0033554
###*protein modification by small protein conjugation or removal - A protein modification process in which one or more groups of a small protein, such as ubiquitin or a ubiquitin-like protein, are covalently attached to or removed from a target protein
###**http://amigo.geneontology.org/amigo/term/GO:0070647

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

Dbashour Week 14

2017-12-08T05:34:39Z

Dbashour: syntax

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

===Summary===
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

Dbashour Week 14

2017-12-08T05:33:44Z

Dbashour: syntax

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

====Summary====
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

Dbashour Week 14

2017-12-08T05:31:43Z

Dbashour: syntax

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
**'''Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**'''Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data'''.
**'''Genes with a p < 0.001 = 514/6189 records or 8.31% of the data'''.
**'''Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
**'''Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.'''
**'''Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

==Summary==
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

Dbashour Week 14

2017-12-08T05:29:39Z

Dbashour: added week 10 corrections

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: there are 4 replicates for each time point 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
# ''' There are 4 data points for each time point. The total number of data points is 20 '''.
# In the cell AC2, I typed <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
# I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
# In cell AI1, I created the column header dGLN3_SS_full.
# In cell AI2, I typed <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
# In cell AJ2, I typed <code>=((20-5)/5)*(AC2-AI2)/AI2</code> and hit enter.
#* I copied this to the whole column.
# In cell AK2, I typed <code>=FDIST(AJ2,5,20-5)</code>.
#* I copied this to the whole column.
# Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
#* I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Then I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
# I type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
# I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: <code>=IFAK2>1,1,AK2)</code>. I used the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
# I copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
# I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# I typed the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. I copied that equation to the entire column.
# I typed "dGLN3_B-H_p-value" into cell G1.
# I typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter then copied that equation to the entire column.
# I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
# I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

* '''''I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.'''''

==== Sanity Check: Number of genes significantly changed ====

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

* I went to my dGLN3_ANOVA worksheet.
* I Selected row 1 (the row with my column headers) and selected the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.
'''**Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.
**Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data.
**Genes with a p < 0.001 = 514/6189 records or 8.31% of the data.
**Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.'''

* When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know ''which'' ones. To apply a more stringent criterion to our p values, I performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. I filtered the data to determine this relationship and found:
'''**Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.
**Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.'''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
* I also compared NSR1 and ADH1 and found the following:

===== NSR1 =====
#Unaltered p-value = 0.000506
#Bonferroni-Corrected p-value = 1
#B-H corrected p-value = 0.008167
#Average Log Fold:
#* @ 15 = 3.50622
#* @ 30 = 4.53189
#* @ 60 = 2.75921
#* @ 90 = -1.85027
#* @ 120 = -1.86741
#As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

===== ADH1=====
#Unaltered p-value = 0.772
#Bonferonni-corrected P-Value: 1
#B-H-corrected P-Value: 0.8617
#Average Log Fold
#* @ 15 = -0.902324179
#* @ 30 = -0.692990646
#* @ 60 = 0.138781308
#* @ 90 = -0.045097454
#* @ 120 = 0.514075012
#As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

==Summary==
In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

==Week 10 Corrections==
==== Clustering and GO Term Enrichment with stem ====

# '''Prepare your microarray data file for loading into STEM.'''
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
#** Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. '''''Record the number of genes left in your electronic notebook.'''''
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
#** Save your work. Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
#*** Note that you should turn on the file extensions if you have not already done so.
# '''Now download and extract the STEM software.''' [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
#* Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
#* This will create a folder called <code>stem</code>. Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.

# '''Running STEM'''
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
##* Click on the radio button ''No normalization/add 0''.
##* Check the box next to ''Spot IDs included in the data file''.
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source. Select ''No cross references'', from the Cross Reference Source drop-down menu. Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
## In section 4 (Execute) click on the yellow Execute button to run STEM.
# '''Viewing and Saving STEM Results'''
## A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
##* Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
##** Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
# '''Analyzing and Interpreting STEM Results'''
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. '''''Each member of your group should choose a different profile.''''' Answer the following:
##* '''''Why did you select this profile? In other words, why was it interesting to you?'''''
##* '''''How many genes belong to this profile?'''''
##* '''''How many genes were expected to belong to this profile?'''''
##* '''''What is the p value for the enrichment of genes in this profile?''''' Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
##* Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. '''''How many GO terms are associated with this profile at p < 0.05?''''' The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
##** Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org]. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?'''''
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA_DB.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]

File:DGLN3 ANOVA DB.xlsx

2017-12-08T04:50:57Z

Dbashour:

Dbashour Week 14

2017-12-08T04:34:38Z

Dbashour: /* Week 8 Corrections */ part 1 updated

Dbashour Week 14

2017-12-08T04:03:19Z

Dbashour: /* Week 8 Corrections */ syntax fix

=Electronic Notebook=
==Week 8 Corrections==

*strain: dGLN3
*filename: DGLN3_ANOVA_DB 
*timepoints: 15, 30, 60, 90, 120 
*number of replicates: it seems as though there were supposed to be 5 replicants for each time point but only time point 30 has all 5 while 15, 60, 90, and 120 have 4 replicates. 
*number of NA cells replaced: 6652

==== Part 1: Statistical Analysis Part 1 ====

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.

# I created a new worksheet, named it "dGLN3_ANOVA".
# I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
# At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
# In the cell below the dGLN3_AvgLogFC_t15 header, I typed <code>=AVERAGE(</code>
# Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
# I Clicked on this cell and positioned my cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
# I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
# Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
# In the first cell below this header, I typed <code>=SUMSQ(</code>
# I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
# In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
#'''There are 5 time points for each
Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
# In the first cell below the header dGLN3_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
#* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
#* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
#* The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
#* Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
# Repeat this computation for the t30 through t120 data points. Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
# In the first column to the right of (STRAIN)_ss_t120, create the column header dGLN3_SS_full.
# In the first row below this header, type <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
# In the next two columns to the right, create the headers dGLN3_Fstat and dGLN3_p-value.
# Recall the number of data points from (13): call that total n.
# In the first cell of the dGLN3_Fstat column, type <code>=((n-5)/5)*(<(dGLN3_ss_HO>-<dGLN3_SS_full>)/<dGLN3_SS_full></code> and hit enter.
#* Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints and the dSWI4 strain has 4 timepoints (it is missing t15).
#* Replace the phrase dGLN3_ss_HO with the cell designation.
#* Replace the phrase <dGLN3_SS_full> with the cell designation.
#* Copy to the whole column.
# In the first cell below the dGLN3_p-value header, type <code>=FDIST(<dGLN3_Fstat>,5,n-5)</code> replacing the phrase <dGLN3_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain). Copy to the whole column.
# Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
#* Click on cell A1 and click on the Data tab. Select the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
#* Click on the drop-down arrow on your dGLN3_p-value column. Select "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
#* Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

=== Calculate the Bonferroni and p value Correction ===

# Now we will perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem]. Label the next two columns to the right with the same label, (STRAIN)_Bonferroni_p-value.
# Type the equation <code>=<dGLN3_p-value>*6189</code>, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
# Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second dGLN3_Bonferroni_p-value header: <code>=IFdGLN3_Bonferroni_p-value>1,1,dGLN3_Bonferroni_p-value)</code>, where "dGLN3_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made. Use the Step (10) trick to copy the formula throughout the column.

==== Calculate the Benjamini & Hochberg p value Correction ====

# Insert a new worksheet named "dGLN3_ANOVA_B-H".
# Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
# For the following, use Paste special > Paste values. Copy your unadjusted p values from your ANOVA worksheet and paste it into Column D.
# Select all of columns A, B, C, and D. Sort by ascending values on Column D. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
# Type the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
# Now you can calculate the Benjamini and Hochberg p value correction. Type dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. Copy that equation to the entire column.
# Type "dGLN3_B-H_p-value" into cell G1.
# Type the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and press enter. Copy that equation to the entire column.
# Select columns A through G. Now sort them by your MasterIndex in Column A in ascending order.
# Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.

* '''''Zip and upload the .xlsx file that you have just created to the wiki.'''''

==== Sanity Check: Number of genes significantly changed ====

Before we move on to further analysis of the data, we want to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

* Go to your dGLN3_ANOVA worksheet.
* Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
* Click on the drop-down arrow for the unadjusted p value. Set a criterion that will filter your data so that the p value has to be less than 0.05.
** '''''How many genes have p < 0.05? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
* When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
* We have just performed 6189 hypothesis tests. Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know ''which'' ones. To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. To see this relationship, filter your data to determine the following:
** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?'''''
** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
* We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table. Use this [[Media:BIOL367_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.
** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain. We will do the comparison with wild type at a later date.
* Comparing results with known data: the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?''''' Note that the average Log fold change is what we called "dGLN3_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
* For fun, find "your favorite gene" (from your web page) in the dataset. '''''What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?''''' Does your favorite gene change expression due to cold shock in this experiment?

==Week 10 Corrections==

==Week 10 Continued==

===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

# I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
#* I then copied the list of gene IDs onto my clipboard.
# I launched a web browser and went to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, I clicked on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* I pasted my list of genes from my chosen cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant". '''''How many transcription factors are green or "significant"?'''''
#* There are 30 green or significant transcription factors.
#** '''''I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.'''''
#*** '''''I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.'''''
#*** '''''My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
# For the mathematical model and GRNsight, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
#* I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
#**Sfp1p
#** Msn2p
#**Yhp1p
#**Yox1p
#**Ace2p
#**Gln3p
#**Yap1p
#**Pdr3p
#**Ume6p
#** Pdr1p
#** Stb5p
#** Swi5p
#** YLR278C
#** Mig2p
#** Asg1p
#** Tup1p
#** Gcr2p
#** Msn4p
#** Rim101p
#** Gcn4p
#**Sut1p
#**Mcm1p
#** Met4p
#** Rlm1p
#** Ino4p
#** Ndt80p
#** Zap1p
#** Abf1p
#** Cyc8p
#** Gat3p
#* I then went to the link ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]'' on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
#** Click the "Generate" button.
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.



====Visualizing Your Gene Regulatory Networks with GRNsight====

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
# First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
#* Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
#* Save this file in Microsoft Excel workbook format (.xlsx).
#* I checked to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor. If a factor is not connected to any other factor, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
#** Select the area of the entire adjacency matrix.
#** Click the Data tab and click the custom sort button.
#** Sort Column A alphabetically, being sure to exclude the header row.
#** Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

==== Summary of what you need to turn in for the individual Week 10 assignment ====

# Your individual journal page should have an '''''electronic lab notebook''''' recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
# Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
# Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the [[Week 8]] assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
# Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
#* the file that was saved from your original spreadsheet that you used to run stem
#* each of the genelist and GOlist files for each of your significant profiles.
# Write a paragraph-length conclusion for this week's exercise.

= Deliverables =
[[Media:DGLN3_ANOVA.xlsx | DGLN3 ANOVA/Stem]] 
[[Media:DGLN3_ppt_Dina.pptx | DGLN3 ppt Dina]] 
[[Media:Gene_List_GO_List_DB.zip | DGLN3 Gene List and GO list]] 
[[Media:Yeastract_results_TF_DB_Gene_hAPI.xlsx | Yeastract TF List]] 
[[Media:GRNmap_dGLN3_input.xlsx | GRNmap dGLN3 input]] 
[[Media:GRNmap_dGLN3_output.png | GRNmap dGLN3 output]]