Regressing out genes #492
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Hi,
I'm not entirely sure how Seurat does it, but I assume you could take the mean expression level (or mean z-score) of a couple of genes, store that in a.varcolumn, and regress that out bysc.pp.regress_out(adata, var_col)?Something like this:
adata.var['genes_of_interest'] = adata.X[:,gene_list].mean(0) sc.pp.regress_out(adata, genes_of_interest)If you want to ensure an equal contribution of all the genes to the gene score without weighting by mean gene expression, you could first use
sc.pp.scale()on a copy of theadataobject like this:adata_tmp = adata.copy() sc.pp.scale(adata_tmp) adata.var['genes_of_interest'] = adata_tmp.X[:,gene_list].mean(0) sc.pp.regress_out(adata, genes_of_interest)Note that I have not tested this code... so no guarantees ;).
This would be very easy to implement with:
sc.get.obs_df(adata, keys).That would not solve the problem of (I believe) constant genes giving errors.
In Seurat, there is an option of selecting a list of genes in the pre-processing regressOut function. I was wondering if there was similar functionality in Scanpy. Doing something like below does not work for me as a lot of the cells have 0 expression, giving me a PerfectSeparationError.
adata.obs[gene] = adata[:, adata.var_names==gene].Xsc.pp.regress_out(adata,gene)Any help would be appreciated. Thank you!