Dear all,
In my experimental design, I have 20 libraries, coming from 20 F2 interspecific hybrids, meaning every genotype is different.
10 individuals are tolerant and the other 10 are sensitive to a specific type of stress. I’m interested in what genes are differentially expressed in these two group of plants (tolerant vs sensitive).
Because of the unique genotype of each individual, I notice that the variance of a gene within a group (tolerant or sensitive) is big. As I do have a biological explanation for this, I don’t what to correct for this variance.
This is why I’d like to skip the estimateDespersion() function in DESeq2 and continue with the nbionamWald() function directly after library normalization. However, this function requires dispersion estimates for the estimateDispersions() function.
cds<-estimateSizeFactors(dds)
skipdisp<-nbinomWaldTest(cds)
Error in nbinomWaldTest(cds) : testing requires dispersion estimates, first call estimateDispersions()
I hope anyone can help me out, or has any other suggestions for analyzing these data.
Much appreciated,
Nicky
I’m using R version 3.2.0 (2015-04-16) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 7 x64 (build 7601) Service Pack 1 ‘DESeq2’ version 1.10.1
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PEER normalization after DESeq run
written by tonja.r
You will probably have to pool variances across your samples as you have no replicates. This is technically possible in DESeq2 (see the vignette) but is strongly discouraged by the authors. see http://seqanswers.com/forums/showthread.php?t=31036
Also, see the edgeR Users Guide, it also has suggestions for dealing with designs without replicates. The best suggestion is, however, do perform biological replicates.
You should link to Bioconductor post if you cross post
https://support.bioconductor.org/p/83913/
Otherwise people on one or the other site will spend time answering a question that has already been answered.