Entering edit mode
Hi,
I'm running a RNAseq experiment comparing two different conditions on the same subject... my count matrix is some like that (but much bigger):
341_MO_r150 341_SP_r150 345_MO_r129 345_SP_r129 350_MO_r178 350_SP_r178
A1BG 0 0 2 0 50 0
A1BG-AS1 0 0 0 11 3 0
A1CF 0 0 0 0 37 0
A2M 1 0 0 0 0 0
A2M-AS1 1 0 0 0 9 0
A2ML1 0 0 1 0 6 0
After creating a DESeq2 object in that way...
> dds <- DESeqDataSetFromMatrix(countData = as.matrix(cnt), colData = mde, design = ~ condition+ subject)
> dds <- dds[rowSums(counts(dds)) >= 10,]
> dds <- DESeq(dds, parallel = T)
> res <- results(dds)
> plotMA(res, ylim = c(-2,2), main = "res")
... I'm getting this (very) weird MA-plot:
Any idea what I'm missing, please? Thanks a lot!
Thanks for so quick response, Michael. Although I didn't indicate a specific contrast, the DE is correctly comparing my two conditions:
Ok, I just was using the code you pasted which is different than the code you used (
~condition+subject
above).Otherwise, the plot above may reflect some confounding. What are the library sizes across the two conditions?
In theory, library sizes are equal since both conditions were simultaneously processed.
I'd look into possible confounding of counts with your condition of interest, unless you have reason to believe it is not artifactual that low count genes should always favor SP. Take a look and perhaps consult a bioinformatician who could help diagnose technical problems.
Thanks, Michael. I've plotted the number of reads and the CT values before library preparation and there's an effect on the CT values... do you think that could explain this?
I'm not going to be able to help diagnose the problem here - I'm just too busy and can only provide software support right now.
Ok, thanks anyway for your help!