data_transform_quantile: Quantile-normalize CPM for Each Gene

In jhsiao999/peco: A Supervised Approach for Predicting Cell Cycle Progression Using Single-Cell RNA-seq Data

Description

Quantile-normalize CPM for Each Gene

Transform counts by first computing counts-per-million (CPM), then quantile-normalize CPM for each gene.

Usage

data_transform_quantile(sce, ncores = 2)

Arguments

sce	SingleCellExperiment object.
ncores	Argument passed to makeCluster specifying the number of threads.

Value

SingleCellExperiment object with an additional “cpm_quant” slot; this is added if it doesn't already exist.

Author(s)

Joyce Hsiao

Examples

library(SingleCellExperiment)
data(sce_top101genes)

# Perform CPM normalization using scater and quantile-normalize
# the CPM values of each gene to normal distribution.
sce_top101genes <- data_transform_quantile(sce_top101genes, ncores=2)

plot(y=assay(sce_top101genes, "cpm_quantNormed")[1,],
     x=assay(sce_top101genes, "cpm")[1,],
     xlab = "CPM before quantile-normalization",
     ylab = "CPM after quantile-normalization")

jhsiao999/peco documentation built on Nov. 21, 2020, 5:34 p.m.