1
$\begingroup$

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!

Improve this question
$\endgroup$
2
  • $\begingroup$ 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
    Feb 23 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 ♦
    Feb 24 at 18:47

1 Answer 1

Reset to default
1
$\begingroup$

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):

https://satijalab.org/seurat/articles/pbmc3k_tutorial.html

Improve this answer
$\endgroup$
1
  • 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

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.