data_transform_quantile: Transform counts by first computing counts-per-million (CPM),...

In peco: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

Description

For each gene, transform counts to CPM and then to a normal distribution.

Usage

data_transform_quantile(sce, ncores = 2)

Arguments

sce	SingleCellExperiment Object.
ncores	We use doParallel package for parallel computing.

Value

SingleCellExperiment Object with an added slot of cpm_quant, cpm slot is added if it doesn't exist.

Author(s)

Joyce Hsiao

Examples

# use our data
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 bbefore quantile-normalization",
    ylab = "CPM after quantile-normalization")

peco documentation built on Nov. 8, 2020, 8:16 p.m.