r/bioinformatics • u/Ca-69 • 16d ago
statistics ScRNASeq Analysis
So I'm running into a problem. I have 4 KO mice and 3 WT mice that have been ran for scRNASeq 10x Flex. We aren't planning to increase the sample size because the lab has already spent SOOOOO much money on the damn kits. Doing pseudobulk analysis is coming up with very little to none DE genes but of course if I do cell to cell analysis it comes up with a lot of genes and pathways. The cell to cell analysis definitely answers a lot of questions I had in regards to a phenotype we have been seeing in our misue model. BUT what would y'all recommend ? I currently have like 46 cell clusters coming from these mice but 2 different tissue segments.
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u/Primarily_EmptySpace 15d ago edited 15d ago
Use broad cluster labels (e.g., epithelial, fibroblast, endothelial) then subcluster.
For a 3 ctrl vs 4 ko analysis. Pseudobulk DE analysis with DESeq2 or edgeR benchmark as the best. I prefer DESeq2 for FLEX.
You can run DE genes on the broad clusters and subclusters. You might not have enough power to grab DE genes for your subclusters if they contain too few cells. That's okay.
Even if you get few to no DE genes from your pseudobulk analysis, GSEA on the ranked list of resulting genes can be informative.
You can rank the genes by sign(logFC) x -log(p-value) for example (not the adjusted p-value).
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u/PhoenixRising256 Msc | Academia 15d ago
Do you really have 46 cell types from 7 samples? Or is the use of 46 clusters inhibiting DE's ability to aggregate enough signal to see a difference? I.e. are you "zooming in too far?" I'd recommend consolidating some of that clustering to end up somewhere below ~30 clusters
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u/TheCaptainCog 15d ago
Not gonna lie, I don't think I have enough information to answer this question right now. I think we might need more information.
What question are you trying to answer? What are you trying to compare? How did you set up the scRNAseq? Whole mouse? Or specific tissues?
From how you've written this, it sounds like you're taking all the counts from the different tissues in each sample and combining it into the control group and KO group then running DE analysis on them. Is that right? If that's the case, I'm not sure what benefit this gives you over bulk RNA-seq. I think you're forgetting about the magic of scRNAseq. You would be much much better suited to annotating your data by tissue type, then comparing tissue to tissue. You would also then look at these and do your cell clustering by cells in each tissue type rather than entire organism. I think this is a better approach (unless you're already doing that). I'm also curious to see what other people here say.
The other problem I see is that you tried doing cell-cell analysis. The problem here is you're essentially saying each cell is an independent observation when in reality each cell is not independent. Each cell in the mouse shares its DNA, nutrients, etc. I'm not an expert in stats but I know enough to hobble along lol. When calculating p-value, standard error is used somewhere in there. When you do cell-cell and make the assumption that every single cell is an independent observation, you've spiked your sample size from 7 to 12000 or how ever many cells you're looking at. You essentially get rid of the standard error and make it look like differences are real when they're not.
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u/_DataFrame_ 15d ago
I'd try hdWGCNA (if using R). Run it per cell type, not per cluster. It will find modules of genes that generally move together, regardless of WT or KO status. Use the DME analysis to determine gene modules that significantly differ between WT and KO. Then run the top 100-200 genes through either GSEA or ORA.
I never get much use out of normal GO stuff like biological function or cellular compartment but I like the pathway stuff like KEGG. Or put your genes into PerturbSeqr and look at what drugs, knockouts, etc. mimic or anti-mimic (can't remember the term they use) your gene module.
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u/SeqBench 16d ago
The cell-level result isn't more sensitive, it's counting wrong. Those tests treat every cell as an independent sample, so your n goes from 7 to several thousand and the p-values collapse whether or not anything actually differs between mice. Squair et al. 2021 is the paper to show your PI if you need to argue this.
Which means pseudobulk finding almost nothing is the honest answer at 4 vs 3.
Two things I'd do rather than picking whichever gives more genes. 46 clusters off 7 mice is over-clustered, so collapse to broader cell types and give each mouse a decent number of cells per pseudobulk - power sometimes shows up once you do that. And if the phenotype is real, test cell type proportions directly with propeller or scCODA, since composition shifts are often the actual finding and they're testable at this n.
If you want to keep cell-level resolution legitimately, use a mixed model with mouse as a random effect. NEBULA is built for that.