makeSignMatrix: Create a new SingleCellExperiment object for sign-by-sample...

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

Create a new SingleCellExperiment object for sign-by-sample matrices.

Description

This function creates a new SingleCellExperiment object for sign-by-sample matrices (SSM) by concatenating SSMs for strongly and variably correlated gene sets.

Usage

makeSignMatrix(sce = NULL, weight_strg = 0.5, weight_vari = 0.5)

Arguments

sce	A SingleCellExperiment object.
weight_strg	A weight parameter for strongly correlated gene sets.
weight_vari	A weight parameter 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$GO, min_cnt_strg = 2, min_cnt_vari = 2)
pbmcs$GO <- makeSignMatrix(sce = pbmcs$GO, weight_strg = 0.5,
                           weight_vari = 0.5)
# The resutls can be check by, e.g., assay(pbmcs$GO, "counts").

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