Map Eigengenes on a Seurat Object — map_eigengenes_on

Use scores to calculate module scores for feature expression programs in single cells and applies to Seurat object using UCell::AddModuleScore_UCell()

Usage

map_eigengenes_on_seurat(
  seurat_obj,
  membership,
  cutoff_method = c("value", "top_gene", "both"),
  value_cutoff = 0.75,
  top_genes_cutoff = 10,
  assay = "RNA",
  slot = "data",
  prefix = NULL,
  suffix = "_UCell",
  ncores = 1
)

Arguments

seurat_obj: Seurat Object
membership: a data.frame or matrix of continuous values, with genes as rows and communities as columns. Often this is the community_membership or full_community_membership output from icwgcna().
cutoff_method: should cutoff be based on a value, number of top genes, or both method. Both method will apply the top gene method, but only for genes higher than the value_cutoff.
value_cutoff: value cutoff (ignored when cutoff_method = "top_gene")
top_genes_cutoff: number of top genes to include (ignored when cutoff_method = "value")
assay: Seurat object assay element to use
slot: Pull out data from this slot of the Seurat object
prefix: prefix to add to column names of the Seurat object meta.data
suffix: suffix to add to column names of the Seurat object meta.data
ncores: Number of processors to parallelize computation for UCell::AddModuleScore_UCell()

Value

Seurat Object with additional meta.data columns of community results

Details

For typical Seurat objects this function may take a few minutes if only using one core.

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)

unique_top_genes <- map_eigengenes_on_seurat(
    SeuratObject::pbmc_small,
    results$community_membership
    )
} # }