gdcVoomNormalization: TMM normalization and voom transformation

In GDCRNATools: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

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)

GDCRNATools documentation built on Nov. 27, 2020, 2 a.m.