I have RNA-Seq data for 116 tumor and 67 normal samples. Aprrox. 56k genes. Differential analysis can be done b/w tumor and normal with edgeR/ Deseq2.
Among those 56k genes, I'm particularly interested in looking at expression of 30 genes between tumor and normals.
So, for that do I need to subset the counts data of those 30 genes and then do differential analysis?
(OR)
Differential analysis should be done for all 56k genes and then check whether those interested 30 genes are differentially expressed or not?
I tried this to ways. When I did differential analysis with those 30 genes I found some genes differentially expressed in which I'm interested in. But when I did differential analysis with all the 56k genes, I didn't find those genes in which I'm interested in.
What is the right way to do if I want to look at expression of some specific genes?
And what if I want to look the expression of a single gene between tumor and Normal - do I still need to go with edgeR/DEseq2 or t-test is better?
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written
by
Vasu
1 answer
You should always performed analysis on all genes and then extract information concerning your genes of interest. DESeq or edgeR will use the information of all gene read count for the expression analysis. If you do not use all genes you will introduce big biais such as depth normalization.
Yes it is the same. You should always use all genes for the analysis. And then check the genes of interest.
normalized read count should be ok. Best would be to plot the results as a boxplot for example. check DESeq2 vignette for more information : https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis
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