Can I test for differential expression using FPKM values?

[JonB]

Hi,

I know this subject has been discussed in many posts, and I am sorry if I am double posting.

I have a matrix of gene expression counts and the values are FPKM.
I don't have the sam files.

Can I use cuffdiff with this input?

I can run DESeq2 the way it is described in the tutorial, but it says that the input should be raw counts. What happens if the input is FPKM?

Are there other methods to run DE-test on FPKM values?

Thanks,

Jon

[dpryan]

If you input FPKMs into DESeq2 you'll get unreliable results, since you're violating the statistical premise. You can probably use voom() to get things ready for analysis in limma. That's what I'd try first.

[JonB]

Ok, thanks!

[JonB]

Sorry for spamming you with questions...

I am able to convert the read counts by voom(), but I have trouble getting the experiment design correct using model.matrix()

I have 18 libraries representing two different conditions: 'normal' and 'post-inversion'. My design looks like this now:
design
I think I have to create a factor with the two labels 'normal' and 'post-inversion'?

[JonB]

And now I just realized there's a bioconductor mailing list which is probably where this question belong

[dpryan]

Yeah, you can more easily ask the authors on the list If you're just interested in the normal vs. post-inversion comparison (rather than also looking at whatever that "Sycon2" thing is), then yes, just creating a factor with those levels (design$libType will provide that, though you probably named the dataframe something else).

[Simon Anders]

Just for the record: "voom" expects counts the same as edgeR and DESeq do. See also https://stat.ethz.ch/pipermail/bioco...er/056309.html

[atulchandra]

Could you tell me anyone, why we use TPM, FPKM or RPKM normalization in single-cell expression analysis?

Is there any biological explanation????

Thanks in advance.!