Hivanson Week 9

Methods/Results

Strain name: ∆CIN5 strain

Filename: HI_BIOL367_S24_microarray-data_dCIN5.xcls

Number of replicates per strain: 4

Timepoints: 15 minutes, 30 minutes, 60 minutes, 90 minutes, 120 minutes

Statistical Analysis Part 1: ANOVA

1. Create a new worksheet, naming it "(STRAIN)_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA" or "dCIN5_ANOVA".
2. Copy all data from the "Master_Sheet" worksheet and paste it in your new worksheet.
3. At the top of the first column to the right of your data, create five column headers of the form (STRAIN)_AvgLogFC_(TIME) where STRAIN is your strain designation and (TIME) is 15, 30, 60, 90, and 120.
4. In the cell below the (STRAIN)_AvgLogFC_t15 header, type =AVERAGE(
5. Then highlight all the data in row 2 associated with t15, press the closing paren key (shift 0),and press the "enter" key.
6. This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
7. Click on this cell 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.
8. Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
9. Now in the first empty column to the right of the (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
10. In the first cell below this header, type =SUMSQ(
11. Highlight all the LogFC data in row 2 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
12. In the next empty column to the right of (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
13. Make a note of how many data points you have at each time point for your strain. For most of the strains, it will be 4, but for the wild type it will be "4" or "5". Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, for wt it should be 23 (double-check).
14. In the first cell below the header (STRAIN)_ss_t15, type =SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2 and hit enter.
    • The COUNTA 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.
15. 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.
16. In the first column to the right of (STRAIN)_ss_t120, create the column header (STRAIN)_SS_full.
17. In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
18. In the next two columns to the right, create the headers (STRAIN)_Fstat and (STRAIN)_p-value.
19. Recall the number of data points from (13): call that total n.
20. In the first cell of the (STRAIN)_Fstat column, type =((n-5)/5)*(<(STRAIN)_ss_HO>-<(STRAIN)_SS_full>)/<(STRAIN)_SS_full> 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.
    • Replace the phrase (STRAIN)_ss_HO with the cell designation.
    • Replace the phrase <(STRAIN)_SS_full> with the cell designation.
    • Copy to the whole column.
21. In the first cell below the (STRAIN)_p-value header, type =FDIST(<(STRAIN)_Fstat>,5,n-5) replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. Copy to the whole column.
22. 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 (STRAIN)_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.
    • Be sure to undo any filters that you have applied before making any additional calculations.

How many genes have p < 0.05? and what is the percentage (out of 6189)?

2290 genes; 37.0%

How many genes have p < 0.01? and what is the percentage (out of 6189)?

1380 genes; 22.3%

How many genes have p < 0.001? and what is the percentage (out of 6189)?

691 genes; 11.2%

How many genes have p < 0.0001? and what is the percentage (out of 6189)?

358 genes; 5.8%

How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?

151 genes; 2.4%

How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?

1453 genes; 23.5%

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?

Unadjusted p value: 6.37625E-08

Bonferroni-corrected p value: 0.000394626

B-H-corrected p value: 2.19237E-05

average Log fold change @ 15 minutes: 4.070025

average Log fold change @ 30 minutes: 3.611475

average Log fold change @ 60 minutes: 4.2985

average Log fold change @ 90 minutes: -2.900925

average Log fold change @ 120 minutes: -0.9315

NSR1 shows increased expression from time 15 minutes through 60 minutes. At 90 minutes, NSR1 expression decreases, and at 120 minutes, the expression of NSR1 remains decreased.

What is IMD3's unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is IMD3's average Log fold change at each of the timepoints in the experiment?

Unadjusted p value: 0.111670609

Bonferroni-corrected p value: 1

B-H-corrected p value: 0.232000469

average Log fold change @ 15 minutes: 1.638433333

average Log fold change @ 30 minutes: -0.100766667

average Log fold change @ 60 minutes: 1.659233333

average Log fold change @ 90 minutes: -0.608333333

average Log fold change @ 120 minutes: -0.168133333

Data & Files

Excel microarray data

p value table slide for ∆CIN5

References

• Hivanson
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