Quantile-normalize CPM for Each Gene

Source: R/data_transform_quantile.R

Quantile-normalize CPM for Each Gene

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

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.

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)
#> computing with 2 threads

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