compute_sepI_all: Compute separation indices for each cluster against the...

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

Compute separation indices for each cluster against the others.

Description

This function computes separation indices for each cluster versus the others.

Usage

compute_sepI_all(sce = NULL, labels = NULL, nrand_samples = NULL)

Arguments

sce	A SingleCellExperiment object.
labels	A vector of labels of all the samples (cells).
nrand_samples	An integer for the number of samples used for random sampling, which samples at least one sample per cluster.

Value

A SingleCellExperiment object.

Examples

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_all(sce = pbmcs_eg$GO, labels = labels,
                                nrand_samples = 10)
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

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