estimateSizeFactors: Estimate the size factors for a DESeqDataSet
In aghozlane/DESeq2shaman: Differential gene expression analysis based on the negative binomial distribution

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimate the size factors for a DESeqDataSet

## S4 method for signature 'DESeqDataSet'
estimateSizeFactors(object,locfunc=median,geoMeans,controlGenes,normMatrix)

## S4 method for signature 'DESeqDataSet'
estimateSizeFactors(object, locfunc = median,
  geoMeans, controlGenes, normMatrix)

`object`	a DESeqDataSet
`locfunc`	a function to compute a location for a sample. By default, the median is used. However, especially for low counts, the `shorth` function from the genefilter package may give better results.
`geoMeans`	by default this is not provided and the geometric means of the counts are calculated within the function. A vector of geometric means from another count matrix can be provided for a "frozen" size factor calculation
`controlGenes`	optional, numeric or logical index vector specifying those genes to use for size factor estimation (e.g. housekeeping or spike-in genes)
`normMatrix`	optional, a matrix of normalization factors which do not control for library size (e.g. average transcript length of genes for each sample). Providing `normMatrix` will estimate size factors on the count matrix divided by `normMatrix` and store the product of the size factors and `normMatrix` as `normalizationFactors`.

This function estimates the size factors using the "median ratio method" described by Equation 5 in Ander and Huber (2010). The estimated size factors can be accessed using sizeFactors. Alternative library size estimators can also be supplied using sizeFactors.

Typically, the function is called with the idiom:

dds <- estimateSizeFactors(dds)

See DESeq for a description of the use of size factors in the GLM. One should call this function after DESeqDataSet unless size factors are manually specified with sizeFactors. Alternatively, gene-specific normalization factors for each sample can be provided using normalizationFactors which will always preempt sizeFactors in calculations.

Internally, the function calls estimateSizeFactorsForMatrix, which provides more details on the calculation.

The DESeqDataSet passed as parameters, with the size factors filled in.

Simon Anders

Reference for the median ratio method:

Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 11 (2010) R106, http://dx.doi.org/10.1186/gb-2010-11-10-r106

estimateSizeFactorsForMatrix

dds <- makeExampleDESeqDataSet(n=1000, m=12)
dds <- estimateSizeFactors(dds)
sizeFactors(dds)

dds <- estimateSizeFactors(dds, controlGenes=1:200)

m <- matrix(runif(1000 * 12, .5, 1.5), ncol=12)
dds <- estimateSizeFactors(dds, normMatrix=m)
normalizationFactors(dds)[1:3,1:3]

geoMeans <- exp(rowMeans(log(counts(dds))))
dds <- estimateSizeFactors(dds,geoMeans=geoMeans)
sizeFactors(dds)