This question is related to a previous query regarding pseudobulking in a single-cell dataset.

Question 1

When performing differential expression testing, how can I determine if my selections for filterByExpr min.count and min.total.count are appropriate?

From what I understand based on discussions about filterByExpr, the default min.count and min.total.count values are generally recommended for most scenarios. However, there is an example in Section 4.10.3 of the EdgeR user's guide where these values are explicitly set in the function call:

keep.genes <- filterByExpr(y, group = y$samples$cluster, 
                           min.count = 10, min.total.count = 20)

Assuming I'm using glmQLFit and glmQLTest as demonstrated in the example in Section 4.10.3, how can I determine if these values are too high or too low? Additionally, when would you suggest modifying the default values?

Question 2

The EdgeR manual mentions that "glmQLFit gives special attention to handling of small counts and zero counts."

Does this imply that glmQLFit and glmQLTest can be used without filtering out low counts? Would you recommend this approach?

Thanks in advance!

The limma and edgeR User's Guides, case studies, documentation and online posts all consistently advise you to use filterByExpr with default settings. See for example

• filterbyexp() in edgeR
• Filter genes by expression for voomLmFit

filterByExpr appears many times in the edgeR User's Guide, all but once with default settings. In the one example that you quote, min.total.count was increased to 20 because there were clusters with only 1 sample, so the author wanted to make sure there were at least 20 reads for each gene. To be honest, I don't think that was necessary and the argument could have been left at the default.

You are worrying about this unnecessarily. Sure, you can reduce the amount of filtering without adversely affecting an analysis. But why bother? You will generally just end up with including more non-DE genes in the analysis and perhaps reduce power a little bit.

The filterByExpr settings were chosen with small experiments in mind. The only time I would think about changing the parameter would be for experiments with a large number of samples. In that case, I might consider reducing min.count and min.prop. To judge whether the settings are appropriate, you simply look at the standard edgeR variance plots from plotBCV and plotSA.