**10**wrote:

Hi all, I'm right now working with a RNA-seq raw count data file (in .txt format). It's a matrix of ~50,000 genes (row) and 8 samples (column).

My assignment is to get differential gene expression analysis. But I'm not really sure what it means by that. I'm using DESeq2 package in R for this task, and since I'm really new to R and bioinformatics, I'm not really sure what they want as the result.

So my questions are:

1) What does one expect from differential expression analysis? I've got the list of genes that have p-values less than 0.05. Would that be sufficient for differential expression analysis? What I have right now is just the list of genes.

2) What is MA plot and what does it have to do with differential expression analysis? I've read some papers about RNA-Seq and R packages that help analysis, and most of them mentioned MA plot. What is its significance in DE analysis? And what can we see from it?

Here is part of my source code that I wrote to get the list of genes that have p-values less than 0.05:

#get differential expression results

res<-results(dds)

#sort by p-value

res<-res[order(res$pvalue),]

head(res)

#write a .csv file that contains information of genes

#that have p-value smaller than 0.05

write.csv(res[which(res$pvalue<0.05),], file="DEoutput.csv")

Thanks!

**15k**• written 13 months ago by fromhj304 •

**10**