Dear bioconductor support members,

I have been struggling with the interpretation of differential abundance estimates as output from the diffcyt package. Specifically, I want to use the testDA_edgeR to identify differentially abundant cell populations in single cells cytometry data from ~100 samples between ~10 subgroups. While I am able to compute the logFC for each subgroup, using the "topTable" function of the package only one p-value / adj. p-value is shown, which I am assuming indicates whether there is any subgroup where significant differential abundance occurs for the cluster in question. However, I would like to compute one p-value per comparison (i.e. separate p.values for subgroup B - subgroup A, subgroup C - subgroup A, subgroup D - subgroup A etc.).

In the topTable function from the edgeR / limma packages it is possible to extract one logFC / pvalue per gene / per comparison by using the "coef" argument. However, in this package's implementation of the "topTable" function this functionality seems to be missing. My understanding of the underlying process of differential abundance measurements is that a linear model is fitted using the parameters provided very similar to differential gene expression analysis. So I am wondering if there is a rationale behind this missing functionality and whether there is any way to nevertheless show one p-value per comparison.

I am aware that you could just run the analysis multiple times with one contrast matrix per comparison, but this seems like a very roundabout way of addressing this.

Since this is not strictly a question about an error / bug I ask for your understanding for not providing a minimally reproducible example and am happy for any recommendations / help

Best Stefan