normalizeData: Normalize transcriptome sequencing data.

Description Usage Arguments Value Author(s) References Examples

View source: R/normalizeData.R

Description

Normalize count-based measures of transcriptome sequencing data using the Trimmed Means of M-values (TMM) or DESeq approach.

Usage

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normalizeData(data, normalization)

Arguments

data

numeric matrix representing the counts of dimension (g x n), for g genes in n samples.

normalization

Normalization method to be used to correct for differences in library sizes, with choices “TMM” (Trimmed Mean of M-values), “DESeq” (normalization method proposed in the DESeq package), and “none”

Value

Author(s)

Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic

References

S. Anders and W. Huber (2010). Differential expression analysis for sequence count data. Genome Biology, 11(R106):1-28.

A. Rau, M. Gallopin, G. Celeux, F. Jaffrezic (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics, doi: 10.1093/bioinformatics/btt350.

M. D. Robinson and A. Oshlack (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(R25).

Examples

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library(Biobase)
data("sultan")
normData <- normalizeData(exprs(sultan), norm="DESeq") 

andreamrau/HTSFilter documentation built on Dec. 7, 2020, 2:44 a.m.