remove_signs_manually: Remove signs by specifying keywords.

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

Remove signs by specifying keywords.

Description

This function removes signs by specifying keywords.

Usage

remove_signs_manually(sce = NULL, keywords = NULL)

Arguments

sce	A SingleCellExperiment object.
keywords	keywords separated by pipes '|'.

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$GO, min_cnt_strg = 2, min_cnt_vari = 2)
keywords <- "Covid19|foofoo|hogehoge"
pbmcs$GO <- remove_signs_manually(sce = pbmcs$GO, keywords = keywords)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, and `metadata(pbmcs$GO)$sign_all`.

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