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.
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.
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