gdcVoomNormalization: TMM normalization and voom transformation

Description Usage Arguments Value Author(s) References Examples

View source: R/gdcVoomNormalization.R

Description

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

Usage

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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

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####### 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.