I have a couple of questions about estimation of dispersion in DeSeq2 vs. DeSeq. The paper and manual on DeSeq and DeSeq2 are very clear but I have a hard time understanding what is the ‘correct’ or ‘recommended’ method of estimating the dispersion is for RNA-Seq. Does this depend on the sample size? Are there other assumptions that have to be met for each dispersion estimate type (mean, local or parametric)? is there a particular reason behind ‘tag-wise’ estimation not being in DeSeq2?

Thank you!

Michael,

Thank you for your answer. This clears it up for me.

Best,

JK

Michael,

Is there a reason that the per-sample dispersion calculation was discontinued for DESeq2? I am working with ASD samples which have been observed [ http://dx.doi.org/10.1371/journal.pone.0016715 ] to exhibit lower variance. I need to estimate the ASD and typical dispersion separately. Can I do this with DESeq2?

Ben

It was replaced because we wanted to generalize all the methods for arbitrary experimental designs. DESeq2 doesn't have support for each condition having it's own dispersion. However, you can use DESeq2 to estimate the dispersion of a set of samples, if that is what you are interested in comparing. Create a dds object with the samples of a single condition and use a design of ~1, then run

`estimateSizeFactors`

() and`estimateDispersions`

() and you can access the dispersions with the`dispersions`

() function.Thanks! Let me repost this. I think this is a useful exchange and I need some clarification

Thanks! Let me repost this. I think this is a useful exchange and I need some clarification

Estimating group-specific dispersion in DESeq2