Description Usage Arguments Value Author(s) References Examples
View source: R/normalizeData.R
Normalize count-based measures of transcriptome sequencing data using the Trimmed Means of M-values (TMM) or DESeq approach.
numeric matrix representing the counts of dimension (g x n), for g genes in n samples.
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”
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
Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic
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).
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library(Biobase) data("sultan") normData <- normalizeData(exprs(sultan), norm="DESeq")
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