Hi everybody,
for a research, I measured the expression levels of about 150 genes in 30 patients. Now, I'm interested to see if there is a difference, among three groups of these patients, in the expression levels of a particular gene (only one).
Could I use DESeq2 to do that?
The problem is that I can't use a statistical model directly on the raw data, then I would firstly normalize the raw data of this gene, secondly I want to do a correct statistical test.
I thought to use DEseq2 to normalize my gene using all the 150 genes, then do DESeq2 analysis and finally extract only the result for that gene (ignoring the others).
In your opinion, is it a correct procedures to do?
Thank you very much for your help, and sorry if my english is not correct, but I do not speak it very well.
Thank you again!
Bye
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Then you can just run DESeq() as normal. The 149 other genes should be sufficient to normalize the data, because the library size estimation is robust to a fraction of DE genes (just not when DE is the majority). I'd recommend using fitType="mean" when you have ~100 rows instead of the typical 1000s of rows in the DESeqDataSet.
Yes that is how you get normalized counts.