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.

1 | ```
normalizeData(data, normalization)
``` |

`data` |
numeric matrix representing the counts of dimension ( |

`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” |

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`

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).

1 2 3 | ```
library(Biobase)
data("sultan")
normData <- normalizeData(exprs(sultan), norm="DESeq")
``` |

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