data_transform_quantile {peco} | R Documentation

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

### 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")

[Package peco version 1.6.0 Index]