# J'aime C. Moehlman's Week 12

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*Vibrio cholerae* Data Analysis

#### Normalize the log ratios for the set of slides in the experiment

- entered a new worksheet into our excel file
- pasted all of the compiled raw data into the scaled_centered worksheet
- intserted two rows at the top of the worksheet (above data & below titles)
- in cell A2, we typed "Average" and in cell A3, we typed "StdDev"
- You will now compute the Average log ratio for each chip (each column of data). We did this by using the excel equation "=AVERAGE(B4:B5224)"
- After following that example we computed the average for the rest of the columns
- Then we followed the same steps as above to compute the standard deviation of the log ratios by using the equation "=STDEV(B4:B5224)" and then found the standard deviations for all of the other columns.
- we inserted new colums to the right of each patient sample (i.e. A1- A4, B1-B4, C1-C4) and labelled them each A1-C4_scaled_centered
- In cell C4, we entered this equation: "=(B4-$B$2)/$B$3" and then did the same for every cell in the column, after that we did this for each of the following empty columns

#### Perform Statistical Analysis on the Ratios

- we created a new worksheet called "statistics"
- then we copied all of the gene id's into this new worksheet into column A
- we copied the values from the A1_scaled_centered worksheet
- we created 3 new columns to show the log values.
- we created a new sheet that is designed specifically for genMAPP.

**genMAPP worksheets**

#### Sanity Check

- pvalues less than .05: 5
- pvalues less than .01: 0
- pvalues less than .001: 0
- pvalues less than .0001: 0
- Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there?
- 4

- Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there?
- 1

- There showed to be 1617 log fold changes between -.25 and .25.
- Merrell et al. used the p value as criteria to determine significant gene expression change.
- VC0028 has a p value of .325668
- VC0941 has a p value of about .73
- VC0869 has a p value of about .46
- VC0051 has a p value of about .28
- VC0647 has a p value of about .45
- VC0468 has a p value of about .83
- VC2350 has a p value of about .18
- VCA0583 has a p value of about .29
- These values all seem to be very different, some are significant while others don't really seem to be.