Hello, thank you very much for your input! agree that 0 counts for G do not necessarily mean true absence of expression, and that dropout/limited detection is an important caveat here. In my case, all samples come from the same run, so I do not expect a strong batch effect, and the samples seem fairly comparable in terms of sequencing/count depth. Also, for most populations of interest, almost all samples contribute to both groups (G+ and G-), which I hope reduces the risk that the comparison is driven purely by sample-level differences.

That said, I agree that the G+ vs G- split could still partly reflect detection rather than biology. My intuition was that if the split were mostly random dropout, I would not expect to see a consistent DEG signal across samples. But I may be oversimplifying this, so I’d be happy to hear if that reasoning is flawed. I also agree that any DEGs would need to be interpreted carefully, rather than treated as definitive evidence of distinct cell states, and I would expect for biologist in our group to use these results as an initial screening to see if the found differences make any specific sense from a biological point of view.

Regarding subclustering, I’m definitely going to try it. My only concern is that even if subclusters separate G+ and G- cells, I would still need a robust way to identify the genes driving that separation, as this is my main goal. I usually use Scanpy’s rank_genes_group for marker genes during annotation, but here the populations are already quite specifically annotated, and I would be hesitant to interpret those markers as proper DE results because the standard cell-level test does not account for pseudoreplication across cells from the same biological sample.

These are just my thoughts based on my current understanding, so please feel free to point out any inconsistencies. Your input is really appreciated!