compute.cell.label: 4.2. binarize the label propagation probability in the cell...

In gsdensity: Density-Based Gene Set Specificity Evaluation

4.2. binarize the label propagation probability in the cell population; result in a binarized vector of cells with 'nagative' and 'positive' labels; 'positive' means that the cells are relevant to the gene set

Description

4.2. binarize the label propagation probability in the cell population; result in a binarized vector of cells with 'nagative' and 'positive' labels; 'positive' means that the cells are relevant to the gene set

Usage

## S3 method for class 'cell.label'
compute(cell_vec)

Arguments

cell_vec

output of 'run.rwr'

Value

cell label of 'negative' or 'positive' for a given pathway

Examples

cells <- colnames(pbmc.mtx)
el <- gsdensity::compute.nn.edges(coembed = ce, nn.use = 300)
cv <- gsdensity::run.rwr(el = el,
                         gene_set = gene.set.list[["GOBP_B_CELL_ACTIVATION"]],
                         cells = cells)
cl <- compute.cell.label(cv)

gsdensity documentation built on March 31, 2023, 8:32 p.m.