Dear all, I have an scRNA dataset that I wish to perform differential gene expression analysis on using Seurat. My question relates to the output of FindAllMarkers().

Procedure

I have subset the cells in the Seurat object based on their expression (or not) of an exogenous gene:

> poscells <- WhichCells(object, expression = exogenousgene > 0)
> object$exogenous_exp <- ifelse(colnames(object) %in% poscells, "transfected", "non-transfected")
> exogenous.split <- SplitObject(object, split.by="exogenous_exp")

I make a new assay "integrated" then run SelectIntegrationFeatures(), FindIntegrationAnchors() and IntegrateData(), followed by RunUMAP(), FindNeighbors() and FindClusters().

I then switch back to my "RNA" assay and attempt to look for all marker genes:

> DefaultAssay(combined) <- "RNA"
> genes <- rownames(x = combined)
> combined <- ScaleData(combined, features = genes)
> markers <- FindAllMarkers(combined, grouping.var = "exogenousgene", verbose = TRUE)

Output

My issue is here; when I look at the markers identified, many are listed with numbers appended to the end (e.g., JCHAIN.2 JCHAIN.8 JCHAIN.9, rather than simply JCHAIN). I believe these numbers represent the clusters in which the genes were identified, which is then compared with every other cluster to compute p_val and avg_log2FC.

> markers

JCHAIN.2

JCHAIN.8

JCHAIN.9

CLK1

CD44.6

...

Expected output

That's fine, but instead what I am after is a 'collapsed' version of this output. In other words, comparing the "transfected" and "non-transfected" subsets, which (other) genes are differentially expressed?

> markers

JCHAIN

CLK1

CD44

...

I am new to these tools, so thank you very much for any assistance and kind guidance in the right direction.