Hi everyone,

I would like to use R for performing statistical analysis on RNA-seq data. I have Deseq2 normalized count matrix. I want to perform Fisher-exact test with Benjamini-Hochberg multiple-testing correction to calculate p-values for each genes with respect to different conditions. In that way I want to to check if each genes have significant expression across two different condition WT vs KO.

For examples this table:

```
Gene WT_allele1 WT_allele2 KO_allele1 KO_allele2 **Fisher_Exact_Test(two-sided)**
Xkr4 1 0 0 0 **1**
Gm1992 0 1 0 0 **1**
Rp1 0 0 1 0 **1**
Sox17 1 1 0 2 **1**
Mrpl15 93 94 127 147 **0.506745441**
Lypla1 92 48 87 59 **0.328408209**
Tcea1 290 158 225 149 **0.192618917**
Gm6104 9 21 15 36 **1**
Rgs20 2 1 3 0 **1**
. . . . . .
. . . . . .
. . . . . .
```

so on

I know tools like edgeR, DEseq2, NOIseq and others perform differential expression and provide statistical values. But is it possible if I use fisher exact test and Benjaminiâ€“Hochberg multiple correction to get statistical values for them using R manually? Am I thinking correctly or not? I appreciate any suggestions.

Thanks

Ankit, may I ask why do you want to use Fisher's instead of those tools?

It is mainly because previously we have performed Fisher for our old RNAseq data (not using R). I just wanted to understand that if I can derive same values by using Rscripts/functions.

I would also like to implement the same for my new data. It is mostly for the comparison purposes.

It will be very helpful if I will get the bit of guidance on this.

I understand. However, DESeq, edgeR, or limma should give you more robust results than Fisher's exact test from my understanding. My suggestion is: Perform the Fisher's exact test > correct its p values as @Jean-Karim said below; Compare your significant (FDR < 0.05?) genes with those that com up from DESeq

Thanks. Fisher.test gives only one p-value for contingency table. How will I get gene-wise p-values (as show in the table above or as obtained from DESeq2 normalisation)?