BIOL398-04/S15:Week 11

This journal entry is due on Tuesday, April 7 at midnight PDT (Monday night/Tuesday morning). NOTE that the server records the time as Eastern Daylight Time (EDT). Therefore, midnight will register as 03:00.

NOTE: this page is under construction.

Individual Journal Assignment

• Store this journal entry as "username Week 11" (i.e., this is the text to place between the square brackets when you link to this page).
• Create the following set of links. (HINT: These links should all be in your personal template that you created for the Week 1 Assignment; you should then simply invoke your template on each new journal entry.)
  • Link to your journal entry from your user page.
  • Link back from your journal entry to your user page.
  • Link to this assignment from your journal entry.
  • Don't forget to add the "BIOL398-04/S15" category to the end of your wiki page.

Microarray Data Analysis

For your assignment this week, you will keep an electronic laboratory notebook on your individual wiki page that records all the manipulations you perform on the data and the answers to the questions throughout the protocol. We will be working on the protocols in class on Thursday, March 19 and Thursday, March 26. Whatever you do not finish in class will be homework to be completed by the Week 11 journal deadline.

Background

This is a list of steps required to analyze DNA microarray data.

1. Quantitate the fluorescence signal in each spot
2. Calculate the ratio of red/green fluorescence
3. Log transform the ratios
4. Normalize the ratios on each microarray slide
   • Steps 1-4 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
5. Normalize the ratios for a set of slides in an experiment
   • Step 5 was performed for you using a script in R, a statistics package (see: Dahlquist Lab Protocol :Microarray Data Processing in R
   • You will perform the following steps:
6. Perform statistical analysis on the ratios
7. Compare individual genes with known data
   • Steps 6-7 are performed in Microsoft Excel
8. Pattern finding algorithms (clustering)
9. Map onto biological pathways
   • We will use software called STEM for the clustering and mapping
10. Create mathematical model of transcriptional network
    • The modeling will be performed in MATLAB

For the modeling project, each pair of students will analyze a Dahlquist lab microarray dataset comparing the wild type strain to a different strain of yeast. For the statistical analysis, one member of the pair will analyze the wild type data and one member of the pair will analyze the alternate strain:

• Wild type vs. Δcin5: Will and Jeffrey
• Wild type vs. Δgln3: Tessa and Alyssa
• Wild type vs. Δhmo1: Lucia and Lauren
• Wild type vs. Δzap1: Kara and Kristen
• Wild type S. cerevisiae vs. Wild type S. paradoxus: Natalie and Karina

You will download your assigned Excel spreadsheet from LionShare. You were e-mailed a link to do this before class. Because the Dahlquist Lab data is unpublished, please do not post it on this public wiki. Instead, post the file(s) back to LionShare, which is protected by a password.

Experimental Design

In the Excel spreadsheet, there is a worksheet labled "data". In this worksheet, each row contains the data for one gene (one spot on the microarray). The first column (labeled "ID") contains the gene identifier from the Saccharomyces Genome Database. The second column contains the Standard Name for each of the genes. Each subsequent column contains the log₂ ratio of the red/green fluorescence from each microarray hybridized in the experiment (steps 1-5 above having been performed for you already).

Each of the column headings from the data begin with the experiment name ("wt" for wild type S. cerevisiae data, "dCIN5" for the Δcin5 data, etc., and Spar for the S. paradoxus data). "LogFC" stands for "Log₂ Fold Change" which is the Log₂ red/green ratio. The timepoints are designated as "t" followed by a number in minutes. Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.

The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).

• Begin by recording in your wiki, the strain comparison and individual dataset that you will analyze, the filename, the number of replicates for each strain and each time point in your data.
  • NOTE: before beginning any analysis, immediately change the filename so that it contains your initials to distinguish it from other students' work.

Statistical Analysis Part 1: ANOVA

1. Create a new worksheet, naming it stats
2. Copy the first two columns of the data worksheet (containing ID and Standard Name) into the stats sheet.
3. In the first row, columns c through g, create column labels of the form (STRAIN)_xbar_(TIME) where (STRAIN) is wt, dGLN3, etc., and (TIME) is 15, 30, etc.
4. In the first row, columns h and i, create the column labels (STRAIN)_xbar_grand and (STRAIN)_ss_HO.
5. In the first row, columns j through n, create the column labels (STRAIN)_ss_(TIME) as in (3).
6. In the first row, columns o, p, and q, create the column labels (STRAIN)_SS_full, Fstat and p-value.
7. Now we're ready to compute. In cell c2, type =AVERAGE(
8. Then click on the tab containing the data, and highlight all the data in row 2 associated with (STRAIN) and t15, press the closing paren key (shift 0),and press the "enter" key.
9. Click on the tab for the stats sheet. Cell c2 now contains the average of the log fold change data from the first gene at t=15 minutes.
10. Click on cell c2 and position your 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.
11. Move to cell d2, and repeat (7) through (10) with the t30 data, to e2 with the t60 data, f2 with the t90, g2 with the 120.
12. Move to cell h2, and repeat (7) through (10) highlighting all the data for (STRAIN) in row 2 instead of the individual time points.
13. Now, we move to cell i2. Type =SUMSQ(
14. Click on the data sheet's tab again, and highlight all the data in row 2 for your (STRAIN), press the closing paren key (shift 0),and press the "enter" key.
    • The data highlighted here will be same as in (12).
15. Make a note of how many data points you have at each time point. In most cases this number will be 4, but for some strains and times it may be 5. Count carefully. Also, make a note of the total number of data points. For most strains this number will be 20, but for wt it may be 23.
16. In cell j2, type =SUMSQ(data!C2:F2)-4*stats!C2^2 and hit enter.
    • The phrase "data!C2:F2" should be the data associated with t15. The number "4" is the number of data points (note that cells c2, d2, e2, f2 contain 4 data points). The phrase "stats!c2" gets the average you computed in Step (8) for t15, and the "^2" squares that value. Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
17. In cells k2 through n2, repeat (16) 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 (d2,e2,f2,g2) for each time point, and copy the formula to the whole column for each computation.
18. Once you've populated cells j2 through n2, click on o2 and type =sum(j2:n2) and hit enter. Copy to the whole column.
19. recall the number of data points from (15): call that total n.
20. In cell p2, type =((n-5)/5)*(i2-o2)/o2 and hit enter. Don't actually type the n but instead use the number from (20). copy to the whole column.
21. In cell q2, type =FDIST(P2,5,n-5) replacing n as in (20) with the number of data points total. Copy to the whole column.
22. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label column r "STRAIN_Bonferroni_p-value".
23. Type the equation =q2*6189, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
24. Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell s2: =IF(r2>1,1,r2)

Calculate the Benjamini & Hochberg p value Correction

1. Insert a new worksheet named "B&H".
2. Create an index column by first typing "Index" into cell A1. Then type "1" into cell A2 and "2" into cell A3. Select both cells A2 and A3. Click, hold, and then drag the lower right hand corner until you reach row 6190. Release once you get to row 6190.
3. Copy and paste the column of Gene ID's from one of the previous worksheets into column C.
4. For the following, use Paste special > Paste values. Copy Column Q (the unadjusted p values) from the stats worksheet and paste it into Column D.
5. Select all of column D. Sort by ascending values. You can click the menu option Data < Sort... In the new window that opens, keep the option "Expand the selection" and click "Sort...". Keep the "Sort by..." option as "Ascending" and click "OK." Alternately, you can click the sort button from A to Z on the toolbar, keep the option "Expand the selection" and click "OK".
6. Calculate the Benjamini and Hochberg p value correction. Type "STRAIN_BH_pval" in cell E1. Type the following formula in cell E2: =(D2*6189)/A2

and press enter. Copy that equation to the entire column using the trick you learned last week.

1. Type "STRAIN_BH_pval" into cell F1.
2. Find which genes are significantly differentially expressed. Type the following formula into cell F2: =IF(E2<0.05,1,0)

and press enter. Copy that equation to the entire column using the trick you learned last week.

Shared Journal Assignment

• Store your shared journal entry in the shared Class Journal Week 11 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first :) )
• Link to your journal entry from your user page.
• Link back from the journal entry to your user page.
• Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
• Add the "BIOL398-04/S15" category to the end of the wiki page (if someone has not already done so).

View

Now that you've done your own microarray data analysis, we will revisit the case "Deception at Duke".

• View the video: The Importance of Reproducible Research in High-Throughput Biology: Case Studies in Forensic Bioinformatics.
• View the slides from DataONE on data entry and manipulation.
• Optional: for more information on the Duke saga, see the web site put together by Baggerly and Coombes here.

Reflection

• What were the main issues with the data and analysis identified by Baggerly and Coombs? What best practices enumerated by DataONE were violated? Which of these did Dr. Baggerly claim were common issues?
• What recommendations does Dr. Baggerly recommend for reproducible research? How do these correspond to what DataONE recommends?
• Do you have any further reaction to this case after viewing Dr. Baggerly's talk?
• Go back to the methods section of the paper you presented for journal club. Do you think there is sufficient information there to reproduce their data analysis? Why or why not?