Differential expression between two biological samples sequenced with single-cell RNA-seq with thousands of cells in each

Question

[Naim.Mahi]

Hello,

I need some help regarding differential expression (DE) in single cell RNA-seq data between two biological samples.

Here is the situation: I have two biological samples (you can consider these two samples as two different datasets where one is a disease case and another is control). We sequenced these two samples using single cell protocols and identified around 7000 cells in each of the samples. We have done cell identification and differentiation within each of the samples. Now we would like to see if we can find genes that behaves differently in these two samples.
I know, there are methods/packages that do the DE between cell clusters, but couldn't find any that what I'm looking for. I would really appreciate any idea/suggestion.

Thanks.

[jared.andrews07]

Is there any reason DESeq or EdgeR wouldn't work for this?

[ejm32]

You can use any of the standard DEG packages/algorithms (DESeq2, edgeR, limma/voom, etc.), however the question is should you? This exchange on the Bioconductor support site provides the best rationale for why you should be skeptical/cautious of the analysis you propose. Another option is a mixed effect model to try and account for donor-donor variability, something like MAST.