I am working on the RNA-Seq data of bacterial samples including untreated and 2 drug treatment groups (3 replicates each) following the DESeq2 steps. My question is looking at the dispersion plots including fit type parametric and local, can it be inferred that there is less variability among the genes? Also because the results table also gave about 265 statistically significant genes between the control and treatment 1. It seems that with higher mean counts there is a slight increase in dispersion in the fit type local. Kindly share your feedback on understanding this plot.

Code should be placed in three backticks as shown below

   ddsObj<- DESeqDataSetFromMatrix(countData = Raw_counts,colData = sampleinfo,design = ~Condition)
   idx <- rowSums(counts(ddsObj) >10 ) >3
   table(idx)

   ddsObj<-ddsObj[idx,]
   dds<-DESeq(ddsObj)
   plotDispEsts(dds)
    plotDispEsts(dds,fitType = "local")
   dds$sizeFactor
   res<-results(dds)
   res<-res[order(res$padj),]

Hi suhanya,

It feels to me that you could be mixing up a couple of concepts here.

First, to my knowledge, fitType is an argument of the estimateDispersions function rather than plotDispEsts function. As such, I'm not sure what passing the fitType argument is doing in your code snippet.

Second, the option of changing the default setting fitType = parametric to fitType = local is mainly there to allow for still computing dispersion estimates if the parametric fit fails. This is explained in the package vignette: http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis. As such, comparing dispersion estimates from the parametric fit with the local fit is not comparing variability between treatment groups, as your questions suggests.

Third, your questions seems to be about differential variability between treatment groups. I don't see how these plots would tell you something about that. What you might need is making this plot for the treatment groups separately. However, this is only exploratory, and I can imagine there are better visualizations and tests around for studying differential variability.

Fourth, differential variability is not the same as differential expression. You mention 265 genes with differential expression between groups. This means the average (mean) expression of these genes differs between groups. However, the variability may be the same. To simplify, think of the two-sample t-test. Differential expression would compare the means of the distributions, whereas differential variability would compare the width of the distribution.

Kind regards,

Jeroen