normalizeData: Normalize transcriptome sequencing data.

In HTSFilter: Filter replicated high-throughput transcriptome sequencing data

Description

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

Usage

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

• data.norm A numeric matrix representing the normalized counts of dimension (g x n), for g genes in n samples.

• norm.factor A vector of length n giving the estimated library sizes estimated by the normalization method specified in normalization

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

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

HTSFilter documentation built on Dec. 18, 2020, 2 a.m.