estimateSizeFactorsForMatrix: Low-level function to estimate size factors with robust...

In mikelove/DESeq2: Differential gene expression analysis based on the negative binomial distribution

Low-level function to estimate size factors with robust regression.

Description

Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, if requested, another location estimator) of these ratios (skipping the genes with a geometric mean of zero) is used as the size factor for this column. Typically, one will not call this function directly, but use estimateSizeFactors.

Usage

estimateSizeFactorsForMatrix(
  counts,
  locfunc = stats::median,
  geoMeans,
  controlGenes,
  type = c("ratio", "poscounts")
)

Arguments

counts	a matrix or data frame of counts, i.e., non-negative integer values
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 genefilter 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)
type	standard median ratio ("ratio") or where the geometric mean is only calculated over positive counts per row ("poscounts")

Value

a vector with the estimates size factors, one element per column

Author(s)

Simon Anders

See Also

estimateSizeFactors

Examples

dds <- makeExampleDESeqDataSet()
estimateSizeFactorsForMatrix(counts(dds))
geoMeans <- exp(rowMeans(log(counts(dds))))
estimateSizeFactorsForMatrix(counts(dds),geoMeans=geoMeans)

mikelove/DESeq2 documentation built on April 20, 2024, 5:27 p.m.