Compute Cell Type Enrichments Using panglaoDB Cell Markers — compute_panglaoDB

compute cell type enrichments using panglaoDB cell markers using Fisher test.

Usage

compute_panglaoDB_enrichment(
  membership_matrix,
  K = 100,
  memb_cut = 0.65,
  pangDB = data.table::fread(pangDB_link),
  prolif = prolif_names,
  p_cut = 0.001
)

Arguments

membership_matrix: a community membership (kME) matrix with genes as rows and communities as columns. Often community_membership or full_community_membership output from icwgcna()
K: cutoff for top community genes to include for computing enrichment. Used in an AND condition with memb_cut.
memb_cut: cutoff as a membership score threshold for determining top community genes for computing enrichment. Used in an AND condition with K.
pangDB: panglaoDB cell markers database. Default is to read data from t he url pangDB_link
prolif: list of proliferation genes to check. Default is to use prolif_names
p_cut: p value cutoff for determining importance. If all p values are below p_cut for a community, no cell type is selected

Value

Returns a list with the following items:

top_enr - the most significant cell type from the enrichment scores.
full_enr - all panglaoDB cell type enrichment scores for all communities.

Examples

if (FALSE) { # \dontrun{
library("UCSCXenaTools")
luad <- getTCGAdata(
  project = "LUAD", mRNASeq = TRUE, mRNASeqType = "normalized",
  clinical = FALSE, download = TRUE
)
ex <- as.matrix(data.table::fread(luad$destfiles), rownames = 1)
results <- icwgcna(ex)

pangDB <- data.table::fread(pangDB_link)
compute_panglaoDB_enrichment(results$community_membership, pangDB = pangDB)
} # }