compute_sepI_clusters: Compute separation indices of sign scores for given two...

In okadalabipr/ASURAT: Functional annotation-driven unsupervised clustering for single-cell data

Compute separation indices of sign scores for given two clusters.

Description

This function computes separation indices of sign scores for given two clusters.

Usage

compute_sepI_clusters(
  sce = NULL,
  labels = NULL,
  nrand_samples = NULL,
  ident_1 = NULL,
  ident_2 = NULL
)

Arguments

sce	A SingleCellExperiment object.
labels	A vector of labels of all the samples.
nrand_samples	An integer for the number of samples used for random sampling, which samples at least one sample per cluster.
ident_1	Label names identifying cluster numbers, e.g., ident_1 = 1, ident_1 = c(1, 3).
ident_2	Label names identifying cluster numbers, e.g., ident_2 = 2, ident_2 = c(2, 4).

Value

A SingleCellExperiment object.

Examples

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_clusters(sce = pbmcs_eg$GO, labels = labels,
                                     nrand_samples = 10, ident_1 = 1,
                                     ident_2 = c(0, 2))
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

okadalabipr/ASURAT documentation built on Nov. 25, 2022, 5:40 p.m.