Revision as of 23:16, 3 December 2015

Shewanella oneidensis

Our Gene Database Testing Report

Group Paper - File:Final Report 20151218 2 HMH.docx

Group Members

• Coder: Mary Alverson
• GenMAPP User & Project Manager: Ron Legaspi
• Quality Assurance: Josh Kuroda
• GenMAPP User: Emily Simso

Important Links

Our Files

Our Deliverables

GenMAPP User Milestones

Milestone 1: Startup from Original Microarray Paper and Data

1. Download microarray data in its “rawest” form that you can find. (Consult with Dr. Dahlquist about this.)
2. Verify that the gene IDs in the microarray data match the chosen species and strain that is being used to create the .gdb. (Needs to be done in conjunction with the QA and Coder.)
3. File management: on your team's home page
   • Link to the source of the microarray data
   • Upload the microarray data files to the wiki

Milestone 2: Data Preparation

1. Create a table or list that shows the correspondence between the samples in the experiment and the files you have downloaded.
2. Determine how many biological or technical replicates, and which samples were labeled with Cy3 or Cy5.
3. Create a Master Raw Data file that contains the IDs and columns of data required for further analysis.
4. Consult with Dr. Dahlquist on how to process the data (normalization, statistics)

Milestone 3: On-going Analysis Cycle

This milestone represents the on-going work of the GenMAPP user, as the project gradually converges toward a final version of its gene database and compares its own results to the results in the original microarray paper. (Note: These milestones may continue on to Week 13.)

1. Perform the statistical analysis in Excel.
2. Format the gene expression data for import into GenMAPP.
3. Import data into GenMAPP, create ColorSets, and run MAPPFinder.
4. Document and take notes on test runs with GenMAPP.
   • Use the EX.txt file to help the Coder/Quality Assurance team members to validate the .gdb.
5. Do a journal club outline of the paper so that you can use it in the Discussion section of your group report and your final presentation.
6. Create a .mapp file showing one pathway that is changed in your data.

Week 14 Notes

• Compiling Raw Data and Statistical Analysis
• Created a CompiledRawData Sheet.
• Created a MasterSheet and deleted data with GeneID containing the following:
  1. Number of deletions: 705
• Found the error message #NUM! and replaced with a blank space ("nothing") and there were 2118 replacements made.
• Found the error message #DIV/0! and replaced with blank space ("nothing") and there were 23 replacements made.

File was sent to Dr. Dahlquist: Media:Raw Data Shewanella RARL 20151201.xlsx

Week 12 Feedback

• I have reviewed the work that Emily and you did to compile your raw data this last week and want to acknowledge the hard work that you both put in to organize the files and perform the calculations. I'm putting the feedback here on your page and will put a note on Emily's talk page to refer her to this page.
• In light of the effort that it took to get to this point and to cut down on the additional workload that your particular dataset entails, from this point forward, let's cut out two of the timepoints for the depletion and repletion experiments (ignoring the 10 minute and 40 minute timepoints and keeping the 0, 5, 20, and 60 minute timepoints).
  1. All of your calculations at this point exist as individual files; you need to compile all of the log ratios you computed into a single file. In a new workbook, name the first sheet "CompiledRawData". Name Column A "ID" and copy and paste in the list of IDs from one of your files.
  2. Create a "MasterIndex" column as follows. Insert a new column to the right of the "ID" column and name it "MasterIndex". In this column you will create a numerical index of genes so that you can always sort them back into the same order that they started out in.
     • Type a "1" in cell B2 and a "2" in cell B3.
     • Select both cells. Hover your mouse over the bottom-right corner of the selection until it makes a thin black + sign. Double-click on the + sign to fill the entire column with a series of numbers from 1 to 11520 (the number of spots on the microarray).
  3. Then you need to copy and paste (values) the "log2" column from your individual files. They should be in order Log2FC-CO-rep1, Log2FC-CO-rep2, Log2FC-C0-rep3, Log2FC-C0-rep4, Log2FC-C5-rep1, etc.
  4. The next set of manipulations should be performed in a new sheet called "MasterSheet".
  5. Sort the data A-->Z based on the "ID" column. Delete all rows that have an ID of "Blank", "blank", "gDNA", start with "NC-", start with "ORF". Record how many rows got deleted.
  6. Some of your cells are going to have error messages in them because of the previous calculations you did. Find and replace all of these with nothing, record how many cells that is.
  7. Create a new worksheet called "ScalingCentering" and copy and paste special all of your data into this new sheet. You will perform the scaling and centering operations like you did for the Vibrio cholerae data.
     • Once you have done this, e-mail Dr. Dahlquist and provide a link to your file. Your microarray data has duplicated spots. I have a script that will separate these out so that you can average them as technical replicates.
  8. Create a new worksheet called "Statistics", copy and paste the values into this new sheet.
  9. You will average the technical replicate spots for each sample to get one value for each sample.
  10. You will average the biological replicates for each timepoint to get an average for each timepoint (C0, C5, C20, C60, F5, F20, F60).
  11. You will take the ratio of the average log ratios so that you have values for the Average Log Ratio of C5/C0, C20/C0, C60/C0, F5/C60, F20/C60, and F60/C60). Since this is in log space, to take the ratio, you will actually subtract instead of divide.
  12. You will perform a two-sample T test between C5 and C0, C20 and C0, C60 and C0, F5 and C60, etc. and perform the Bonferroni and Benjamini and Hochberg corrections on these p values. This computation is not the same as what we did for Vibrio. Instead you will use the TTEST function in Excel (see me when you are ready to do this). The corrections will be the same as what you did before.
  13. After that, then you will need to then format the data for GenMAPP and you'll be ready to import it into GenMAPP and run MAPPFinder.
• Let me know if you have any questions.

— Kdahlquist (talk) 13:34, 24 November 2015 (PST)