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So i don't think EWCE filters lowly expressed genes currently, only genes that are totally non-expressed in any cell-type.
drop_uninformative_genes performs differential gene expression across cell-types to determine which genes don't vary sufficiently across cell-types to be informative (and drops them).
In addition, we could add an argument to drop_uninformative_genes that removes genes that are lowly expressed in all cell-types (not just non-expressed). Though since this is at the aggregate level of cell-types, it might make more sense to implement this within the generate_celltype_data function instead.
Alternatively, we could just come up with a combined metric for specificity + mean_exp. This way we wouldn't have to drop any genes, which seems to be the number 1 factor affecting cell-type enrichment when using different CTDs (at least across species).
Alternatively alternatively, when creating the specificity matrix, for each celltype we could identify genes that have low expression and artificially set specificity to 0 in that celltype. Then when we compute specificity quantiles, the gene is retained in the vector but it is not used as a marker for that celltype.
Just to recap for @ss8518, @NathanSkene and I just discussed this and decided that while the strategies I described would be worth exploring in the future, it's not a priority at the moment.
Instead, we'd like you to focus on testing the effect of running drop_uninformative_genes in different conditions:
dge_method=NULL: Don't run any differential gene expression.
dge_method="limma", adj_pval_thresh=<values>: Run DGE with limma across a variety of adjusted p-value thresholds.
dge_method="deseq2": Run DGE with DESeq2.
dge_method="MAST": Run DGE with MAST.
I've just exposed the dge_method argument to users in the dev version of EWCE. You can install it with:
No description provided.
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