gdcVoomNormalization {GDCRNATools} | R Documentation

## TMM normalization and voom transformation

### Description

Normalize raw counts data by TMM implemented in edgeR and then transform it by `voom` in limma

### Usage

    gdcVoomNormalization(counts, filter = TRUE)

### Arguments

`counts` | raw counts of RNA/miRNA expression data
`filter` | logical, whether to filter out low-expression genes. If `TRUE`, only genes with `cpm > 1` in more than half of the samples will be kept. Default is `TRUE`

### Value

A dataframe or numeric matrix of TMM normalized and `voom` transformed expression values on the log2 scale

### Author(s)

Ruidong Li and Han Qu

### References

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139-40.
Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome biology. 2014 Feb 3;15(2):R29.

### Examples

    ####### Normalization #######
    rnaMatrix <- matrix(sample(1:100,100), 4, 25)
    rnaExpr <- gdcVoomNormalization(counts=rnaMatrix, filter=FALSE)

[Package GDCRNATools version 1.6.0 Index]