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==Data and files== | ==Data and files== | ||
[[media:Thiuram_yeast_experiment.xlsx|excel workbook]] | [[media:Thiuram_yeast_experiment.xlsx|excel workbook]] | ||
| + | [[media:Genelist_combined_profiles_FunGals.xlsx]] | ||
== Acknowledgements == | == Acknowledgements == | ||
Revision as of 15:14, 3 December 2019
Combined Individual Journals for Kaitlyn Nguyen and Emma Young (Data Analysts).
Contents
Purpose
The purpose of this assignment is to record our progress towards the FunGals group deliverables as the Data Analysts for this week and the future weeks to come. The purpose of week 12 specifically was to download and adapt the data to the formatting we need for analysis. Then to begin the analysis with ANOVA and preparations for STEM.
Methods and Results: Progress
Progress 11/26/19
Analyzing and Interpreting STEM Results
- Why did you select this profile? In other words, why was it interesting to you?
- We collectively combined the 4 red profiles together because they had similar trends and to increase the number of genes for analysis (called "Red".) Similarly this was done to the 3 green profiles as well (upward trends), with the addition of the blue profile #29 added. We will call this group "Green".
- How many genes belong to this profile?
- Red (composed of Profile #9,26,34,11): 289
- Green (Profile #40,42,18,29): 214
- How many genes were expected to belong to this profile?
- Red (composed of Profile #9,26,34,11): 51.5
- Green (Profile #40,42,18,29): 48.5
- What is the p value for the enrichment of genes in this profile?
- Due to combining the profiles, we do not have p-values for the enrichment of genes in the 2 different groups.
- 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
AltandPrintScreenbuttons 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).
Clustering the data with STEM, as did on Week 9.
- Note that we will make some adjustments to the GO term analysis because stem was not providing GO term names. We are going to use the GO enrichment tool at GeneOntology.org instead.
- Go to http://geneontology.org/.
- For the cluster you want to analyze, open the gene list and copy the list of genes.
- Paste the list of genes into the "Go Enrichment Analysis" box on the right hand side of the GeneOntology.org page.
- Select "Saccharomyces cerevisiae" from the species drop-down menu.
- Click the "Launch" buton.
- Near the bottom of the results page, click on the button to Export "Table".
- This will prompt you to save a .txt file that can be opened in Excel to view your results.
- Use YEASTRACT to generate a candidate gene regulatory network as you did on Week 9.
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.
- Open the gene list in Excel for the one of the significant profiles from your stem analysis. Choose a cluster with a clear cold shock/recovery up/down or down/up pattern. You should also choose one of the largest clusters.
- Copy the list of gene IDs onto your clipboard.
- Launch a web browser and go to the YEASTRACT database.
- On the left panel of the window, click on the link to Rank by TF.
- Paste your list of genes from your 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"?
- Green: 31
- Red: 30
- Copy the table of results from the web page and paste it into a new Excel workbook to preserve the results.
- Upload the Excel file to the wiki and link to it in your electronic lab notebook.
- Are CIN5, GLN3, and/or HAP4 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".
- Green
- CIN5
- 0.2991 % in user set
- 0.0294
- p-value: 0.5492
- GLN3
- 0.3645% in user set
- 0.0324% in YEASTRACT
- p-value: 0.18046
- HAP4
- 0.2383% in user set
- 0.0467% in YEASTRACT
- P-value:0.00031337
- CIN5
- Red
- CIN5
- 0.2111%% in user set
- 0.028% in YEASTRACT
- p-value: 0.9998
- GLN3
- 0.4152% in user set
- 0.0498% in YEASTRACT
- p-value:0.00207752
- HAP4
- 0.2353% in user set
- 0.0623% in YEASTRACT
- P-value:0.000006235
- CIN5
- 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"?
- For the mathematical model that we will build, 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 15-20 transcription factors in it.
- You need to select from this list of "significant" transcription factors, which ones you will use to run the model. You will use these transcription factors and add GLN3, HAP4, and CIN5 if they are not in your list. Explain in your electronic notebook how you decided on which transcription factors to include. Record the list and your justification in your electronic lab notebook. Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
- Go back to the YEASTRACT database and follow the link to Generate Regulation Matrix.
- Copy and paste the list of transcription factors you identified (plus HAP4, GLN3, and CIN5) 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.
- 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).
- 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 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. You can click the "Grid Layout" button to arrange the nodes in a grid, or you can click and drag 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.
- If you have nodes (genes) floating around in the display that are not connected to any other nodes, we need to delete them from the network for the modeling to work properly. Go back to the Excel workbook and network sheet and delete both the row and column with the floating gene's name. Then re-upload the edited file to GRNsight to visualize it. Use this final version in your PowerPoint and subsequent modeling.
Conclusion
The first stage of our group's project was completed via referencing Week 8 and using Microsoft Excel to complete the tasks. The excel file will be located in the FunGals page for viewing and download. We were able to successfully complete the ANOVA analysis and correct found mistakes. We were able to complete a sanity check with results showing. For the sanity check the unadjusted p- values showed 37.2% of the genes had a p<0.05, 18.16% had a p<0.01, 5.04% have p<0.001, and 0.87% have p<0.0001. The sanity check for the Bonferroni-corrected p-value 0.11% of the genes have p<0.05. For the sanity check on the Benjamini and Hochberg-corrected p-value 16.36% go the genes had a p <0.05. Finally we were able to prepare the Data to run STEM in the next step of working on this project.
Data and files
excel workbook media:Genelist_combined_profiles_FunGals.xlsx
Acknowledgements
This section is in acknowledgement to partner Kaitlyn Nguyen (User:knguye66), Michael Armas (User:Marmas), as well as, Iliana Crespin (User:Icrespin), and Emma Young (User:eyoung20). We would also like to acknowledge Dr. Dahlquist (User:KDahlquist) for introducing and teaching the topic and direction of this assignment. Also to acknowledge that this is a shared electronic notebook between Kaitlyn Nguyen and Emma Young.
"Except for what is noted above, this individual journal entry was completed by me and not copied from another source." Knguye66 (talk) 18:49, 20 November 2019 (PST)
"Except for what is noted above, this individual journal entry was completed by me and not copied from another source." Eyoung20 (talk) 16:40, 25 November 2019 (PST)
References
- Dahlquist, K. (2019, November 19). Data Analysis. In Wikipedia, Biological Databases. Retrieved 6:25, November 20, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Data_Analysis
- Dahlquist, K. (2019, November 20). Final Project Deliverables. In Wikipedia, Biological Databases. Retrieved 6:25, November 20, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_12/13https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Final_Project_Deliverables
- Dahlquist, K. (2019, November 19). Week 12/13. In Wikipedia, Biological Databases. Retrieved 6:25, November 20, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_12/13
- Dahlquist, K. (2019, October 17). Week 8. In Wikipedia, Biological Databases. Retrieved 6:30, October 21, 2019, from https://xmlpipedb.cs.lmu.edu/biodb/fall2019/index.php/Week_8
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