create_signs: Define signs for strongly and variably correlated gene sets.

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

Define signs for strongly and variably correlated gene sets.

Description

This function define signs for strongly and variably correlated gene sets.

Usage

create_signs(sce = NULL, min_cnt_strg = 2, min_cnt_vari = 2)

Arguments

sce	A SingleCellExperiment object.
min_cnt_strg	An integer for the cutoff value for strongly correlated gene sets.
min_cnt_vari	An integer for the cutoff value for variably correlated gene sets.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
# The results are stored in `metadata(pbmcs$GO)$sign_all`.

keita-iida/ASURAT documentation built on Nov. 23, 2022, 6:50 p.m.