Hey folks,
I am assessing results of gene-level differential expression testing with DESeq2, and I could use some help. I am trying to determine which fit type to use in (if I understand correctly) defining the curve to shrink dispersions/counts toward. I have 2 conditions (Drosophila wild type or null for gene X) with 3 replicates of each.
In looking at other forum posts on the subject, and at DESeq2 documentation, my understanding is that parametric fit (the default) is usually best for most RNA-Seq data, and matches well with the expected negative relationship between gene-level dispersion and mean count.
I’ve linked the dispersion and MA plots generated when using the parametric fit below. These data look similar to expected plots I see elsewhere – does this fit seem appropriate for my data? I’ve also included the dispersion and MA plots for local and mean fits for comparison.
Parametric Fit: https://imgur.com/a/AGXMYqr
Local Fit: https://imgur.com/a/RnQC1P5
Mean Fit: https://imgur.com/a/RHsxoMY
Also, one of the forum posts I’ve read (link) describes an objective way to evaluate which fit type is best for a given dataset, by comparing the medians of: (absolute value((log(dispGeneEst) - log(dispFit)))) for each fit type. Can these values (dispGeneEst and dispFit) be outputted from the DESeq2 Galaxy tool, or would I need to download and run the software locally in R?
Thank you so much for your time!