I am analyzing publically available scRNA seq datasets on R using Seurat. I have created a seurat object, clustered and annotated different cell types. Now, I am interested in comparing the expression values and percentage of expressing cells within each cluster for a given set of genes I am interested in. I can visualize their expression using DotPlots, feature plots, etc... but how can I generate a table with the expression values (log2FC) for my defined set of genes across the different clusters/cell types? Thanks!

  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Community Bot
    Commented Feb 23, 2023 at 18:39
  • $\begingroup$ Please clarify more precisely what you want, as there is inconsistency in your question. log2FC values are log-transformed fold change values, and represent a change between two expression profiles, i.e. they represent differential expression, not expression. $\endgroup$
    – gringer
    Commented Feb 24, 2023 at 18:47

1 Answer 1


Assuming you're looking for differential expression values, the FindMarkers function within Seurat would be a good thing to try first:

# find all markers distinguishing cluster 5 from clusters 0 and 3
cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
head(cluster5.markers, n = 5)
##                       p_val avg_log2FC pct.1 pct.2     p_val_adj
## FCGR3A        8.246578e-205   4.261495 0.975 0.040 1.130936e-200
## IFITM3        1.677613e-195   3.879339 0.975 0.049 2.300678e-191
## CFD           2.401156e-193   3.405492 0.938 0.038 3.292945e-189
## CD68          2.900384e-191   3.020484 0.926 0.035 3.977587e-187
## RP11-290F20.3 2.513244e-186   2.720057 0.840 0.017 3.446663e-182 

[Note the avg_log2FC column]

More details can be found at the bottom of the Seurat tutorial pages for the PBMC 3K Guided Tutorial, Finding differentially expressed features (cluster biomarkers):


  • 1
    $\begingroup$ Thanks you very much! Yes, that is what I was looking for. I also found the AverageExpression function to look at expression values across clusters $\endgroup$
    – Ahmed M.
    Commented Feb 27, 2023 at 0:40

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