CID.entropy: Normalized Shannon entropy-based "unclassified" assignment

In SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data

Description

CID.entropy calculates the normalized Shannon entropy of labels for each cell among k-nearest neighbors less than four-degrees apart, and then sets cells with statistically significant large Shannon entropy to be "Unclassified."

Usage

CID.entropy(ac, distM)

Arguments

ac	a character vector of cell type labels
distM	the distance matrix, see ?CID.GetDistMat

Value

A character vector like 'ac' but with cells type labels set to "Unclassified" if there was high normalized Shannon entropy.

Examples

## Not run: 
# load data classified previously (see \code{SignacFast})
P <- readRDS("celltypes.rds")
S <- readRDS("pbmcs.rds")

# get edges from default assay from Seurat object
default.assay <- Seurat::DefaultAssay(S)
edges = S@graphs[[which(grepl(paste0(default.assay, "_nn"), names(S@graphs)))]]

# get distance matrix
D = CID.GetDistMat(edges)

# entropy-based unclassified labels labels
entropy = CID.entropy(ac = P$L2, distM = D)

## End(Not run)

SignacX documentation built on Nov. 18, 2021, 5:07 p.m.