I was just wondering if you have considered including an option in scanpy.tl.rank_genes_groups to specify which variables to select for testing, allowing users to select a subset of variables which would or would not be considered in the statistical test.
For context, I'm trying to test between groups of cells while ignoring ribosomal / mitochondrial genes, but retain them in the .var and .X objects for downstream analysis/visualisation. Making a temp object with these variables removed solely for stats testing partially works, but it's confounded by having to further apply the boolean slice to the .raw object as well.
I've been thinking it would be good to add a mask argument to a number of functions. I think mask_vars=~(adata.var["mito"] | adata.var["ribo"]) could work here.
Hi authors,
First off, love scanpy. Big fan.
I was just wondering if you have considered including an option in
scanpy.tl.rank_genes_groups
to specify which variables to select for testing, allowing users to select a subset of variables which would or would not be considered in the statistical test.For context, I'm trying to test between groups of cells while ignoring ribosomal / mitochondrial genes, but retain them in the
.var
and.X
objects for downstream analysis/visualisation. Making a temp object with these variables removed solely for stats testing partially works, but it's confounded by having to further apply the boolean slice to the.raw
object as well.Thanks, K