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 in each individual, I notice that the variance within a group (tolerant or sensitive) is big. As I do have a biological explaination for this, I don’t what to correct for this variance.
This is why I’d like to skip the estimateDespersions() 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 have 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
Agree.
It's worth questioning the proposed extra effort though for the question at hand. That effort at generating more replicates for each genotype in the end won't necessarily give you a substantially different result in your comparison of these 10 genotypes vs those 10 genotypes. It will add precision to the individual genotype estimates, though the within-group across-genotype variability is what drives the significance of the test across groups.
That's a good point, the within-genotype variance that would be estimated by
duplicateCorrelation
is not very informative for the comparison in question.