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!
Determine expression values for a defined list of genes across different clusters in scRNA data using seurat
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
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.Feb 27 at 0:40
log2FCvalues are log-transformed fold change values, and represent a change between two expression profiles, i.e. they represent differential expression, not expression. $\endgroup$