findDropouts: Identify Dropout Events in Single-Cell Expression Data

In khazum/ccImpute: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)

Identify Dropout Events in Single-Cell Expression Data

Description

Determines which zero values within a transposed, log-normalized expression matrix are likely dropout events. The identification is based on a weighted cell voting scheme, where weights are derived from a processed consensus matrix.

Usage

findDropouts(logX, consMtx)

Arguments

logX	A (sparse or dense) numeric matrix representing the transpose of a log-normalized gene expression matrix. Rows correspond to cells, and columns correspond to genes.
consMtx	A numeric matrix representing the processed consensus matrix obtained from clustering analysis.

Value

A two-column matrix (or data frame) where each row indicates the location (row index, column index) of a potential dropout event in the input matrix 'logX'.

Examples

library(scater)
library(BiocParallel)
library(splatter)

sce <- splatSimulate(group.prob = rep(1, 5)/5, sparsify = FALSE, 
        batchCells=100, nGenes=1000, method = "groups", verbose = FALSE, 
        dropout.type = "experiment")
sce <- logNormCounts(sce)
cores <- 2
logX <- as.matrix(logcounts(sce))
w <- rowVars_fast(logX, cores)
corMat <- getCorM("spearman", logcounts(sce), w, cores)
v <- doSVD(corMat, nCores=cores)
BPPARAM = MulticoreParam(cores)
consMtx <- runKM(logX, v, BPPARAM=bpparam())
dropIds <- findDropouts(logX, consMtx)

khazum/ccImpute documentation built on July 26, 2024, 1:13 a.m.