SCPA comparsion (seurat object)

[BartBryant]

Hi

Goal is to compare pathways in cell types in a Seurat object following https://jackbibby1.github.io/SCPA/articles/seurat_comparison.html

The seurat object contains two samples integrated via FindIntgrationsAnchor()

up to the 'SCPA comparison' section, i get an error message (screenshot below) thx in advance

[BartBryant]

sorry actually figured it out by assessing my Seurat Object and finding the correct column name, which in this instance is called 'celltype.Lmod1'

[BartBryant]

also viewed my 'scpa_out' but unsure on ranking in this example is 'HALLMARK_MYOGENESIS' higher in 'SMC_CTRL' or 'SMCs_mut'?

[jackbibby1]

Hi,

If you look at ?compare_seurat there's a longer explanation under the value header, but the fold change (FC) column is calculated from population1 - population2. So that's CTRL-mut in your example i.e. higher in your CTRL condition.

Jack

[BartBryant]

so i assume you can do the inverse by switching the order? for example, to find out highest pathways in mutant, I would list it 'mut-CTRL' over 'CTRL-mut'?

also, can we determine which genes give rise to that type of pathway enrichment score?

Bart

[jackbibby1]

Maybe I'm missing the question, but SCPA calculates the fold changes in both directions anyway. Your FC column contains both positive and negative values, so the negative values are the pathways enriched in your "mut" condition.

There's a bit of a discussion here around why it's quite difficult to identify particular genes driving the signature in SCPA and I would still recommend doing the same thing that I mentioned

[BartBryant]

thanks for the clarification on FC and info on why it is difficult to identify genes driving the signature in SCPA