Johnllopez Week 8

Electronic Lab Notebook

Experimental Design and Getting Ready

The strain comparison I used is dSWI_4, the dat individual dataset that you will analyze, the filename, the number of replicates for each strain and each time point in your data.

Classwork

The first steps I took to complete this assignment were performed in class as I followed along to Dr. Dahlquist's instructions. Note that each time the list below advances 1 number, I performed a save.

1. After initially downloading the Excel document, I went through and deleted all of the columns that did not relate to me and my partner's strain (dsWI4). Then, I went through the data and replaced cells with "NA" with a blank string. There were 3641 replacements.
2. I then created a new worksheet, naming it

Our total n would be 16 because we are analyzing 4 time points and we have 4 replicates.

After letting 16 = n, we applied the two following functions in order to receive our dSWI4_Fstat and dsWI4_p-values: =((n-4)/4)*(Y2-AD2) and =FDIST(AE2,4,n-4).

Next, I filtered through my p-value data to show only p-values less than 0.05. The result was 5475 records found.

Bonferonni and p value Correction

I started this section by creating two new colums with the label "dsWI4_Bonferonni_p-value". Next, I filled the entire first column of that using the following equation: (dSWI4_p-value * 6189) and filled the column AG. Letting that result = AG, I filled the column AE by using the following formula: =IF(AG2>1,1,AG2).

Benjamini and Hochberg p value Correction

To do this, I created a new worksheet to represent the Benjamini and Hochberg p value Correction calculations. I copied the "MasterIndex", "ID", and "Standard Names" columns from the master sheet and the "p-values" sheet from the ANOVA sheet. Then, I sorted these values from smallest to largest by p-value. This was necessary to achieve an index from smallest p-value to largest. Then, I applied the 2 Benjamini and Hochberg p-value correction formulas, which were (D2*6189)/E2 and =IF(F2>1,1,F2). Finally, I put the values in ascending order by MasterIndex, and copied the last column into my ANOVA file.

Sanity Check: Number of genes significantly changed

I then sorted through all of the genes using the following criteria:

1. I saw that 2,802 / 6,189 genes have p <.05, or 45.274%.
2. I saw that 1,842 / 6,189 have p <.01, or 29.762%.
3. I saw that 975 / 6,189 have p < .001, or 15.754%
4. I saw that 512 / 6,189 have p < .0001, or 8.273%.
5. Out of the Bonferonni-corrected p-value, 212 / 6,189 have p < .01, or 3.425%.
6. Out of the Benjamini-Hochberg corrected p-value, 2,076 / 6,189 or 33.543%

My Spreadsheet

File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zipis my document.