Dear Aaron, and all,

after going over the tutorial for detecting differential expression in scRNA-seq data

(https://bioconductor.org/packages/release/workflows/vignettes/simpleSingleCell/inst/doc/de.html)

thought that I could ask you please :

would the same pipeline (that includes edgeR or limma) apply to scRNA-seq data from Drop-seq/10X Genomics ?

given the fact that the number of UMI reads per gene is very low (eg 0, 1, 2, 3, ...10, ... ; very few genes having 10-20 counts). thanks a lot !

-- bogdan

If you add the counts together to create the pseudo-bulk samples, they won't be low any more. So there's no problem if you have enough cells to contribute to the sum in each sample.

Even if you don't have many cells, edgeR would still be able to deal with low counts, provided the dispersion is low (< 0.5). Very high dispersions cause some of the approximations in the QL framework to fail.

If you have low counts and high dispersions, then this is the worst case scenario. edgeR will not be happy, but the same could be said for other methods, so there's not much that can be done here.