# Seurat FindMarkers() output interpretation

I am using FindMarkers() between 2 groups of cells, my results are listed but i'm having hard time in choosing the right markers. Do I choose according to both the p-values or just one of them? If one of them is good enough, which one should I prefer? I am interested in the marker-genes that are differentiating the groups, so what are the parameters i should look for?

             p_val avg_logFC pct.1 pct.2  p_val_adj
UBD   3.080608e-06 1.7755753     1 0.000 0.07683959
CFB   1.262305e-05 1.3989233     1 0.067 0.31485675
RGS13 1.548593e-05 0.9009480     1 0.200 0.38626551
PYDC1 1.896309e-05 0.9622537     1 0.133 0.47299636
BIRC3 4.099414e-05 1.0129472     1 1.000 1.00000000
ICAM1 4.379895e-05 0.8219610     1 0.200 1.00000000


as you can see, p-value seems significant, however the adjusted p-value is not. and when i performed the test i got this warning In wilcox.test.default(x = c(BC03LN_05 = 0.249819542916203, ... : cannot compute exact p-value with ties I am completely new to this field, and more importantly to mathematics. Please help me understand in an easy way.

• You need to look at adjusted p values only. I'm a little surprised that the difference is not significant when that gene is expressed in 100% vs 0%, but if everything is right, you should trust the math that the difference is not statically significant. Why do you have so few cells with so many reads? Is this really single cell data? Jul 30 '20 at 3:24

The p-values are not very very significant, so the adj. p-value. You need to plot the gene counts and see why it is the case. It could be because they are captured/expressed only in very very few cells. VlnPlot or FeaturePlot functions should help. You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more.