DESeq2 with interest only for 1 gene
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fischer87 • 0
@fischer87-10933
Last seen 5.2 years ago

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

 

R deseq2 differential gene expression help • 787 views
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@mikelove
Last seen 1 hour ago
United States

Are there genes in the 150 that you expect to be not differentially expressed? If not, was there any external control (spike-in) included in the sequencing of each sample?

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fischer87 • 0
@fischer87-10933
Last seen 5.2 years ago

Yes, I expect that all the other 149 genes are not differentially expressed. I thought to leave all the 150 genes in the analysis in order to correctly normalize my gene of interest.

Sorry, how can I see the normalized counts? Is it correct to use the command:

dds <- DESeqDataSetFromMatrix(countData=countD, colData=colData, design=~condition)

NormData <- counts(dds, normalized=TRUE)

 

Thank you very much!

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(Note that if you are replying to my answer, you can use the Add Comment/Reply buttons instead of the Add Answer. Add Answer is for answering the top post, which is your own.(

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.

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jshouse ▴ 10
@jshouse-10956
Last seen 9 months ago

@fischer87  That is how I get normalized counts from DESeq2.

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fischer87 • 0
@fischer87-10933
Last seen 5.2 years ago

Thank you very much!!

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For future reference: these type of responses should be added as a comment to the answer you are replying to, which you can do by clicking on the big "ADD COMMENT" link below the text of every answer.

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Sorry!

Thank you very much for your advice, surely in the future i will do that!

Bye

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