estimateSizeFactors: Estimate Size Factors of ChIP-seq Samples

In MAnorm2: Tools for Normalizing and Comparing ChIP-seq Samples

Estimate Size Factors of ChIP-seq Samples

Description

Given a table of raw read counts from ChIP-seq experiments, estimateSizeFactors returns estimated size factors representing relative sequencing depths of the ChIP-seq samples.

Usage

estimateSizeFactors(counts, subset = NULL)

Arguments

counts	A matrix or data frame consisting of read counts. Each row represents an observation (typically a genomic interval) and each column a ChIP-seq sample. Objects of other types are coerced to a matrix.
subset	An optional vector specifying a subset of observations to be used in the estimation process.

Details

This function utilizes the median ratio strategy to deduce size factors (see "References" for details). It's primarily for being used by the MA normalization process to select an optimal baseline sample, and in most cases you don't need to call this function directly. It may help, however, when you want to specify the baseline sample by your own criterion.

Value

estimateSizeFactors returns a numeric vector specifying the size factors.

References

Anders, S. and W. Huber, Differential expression analysis for sequence count data. Genome Biol, 2010. 11(10): p. R106.

See Also

normalize for the MA normalization process.

Examples

data(H3K27Ac, package = "MAnorm2")
attr(H3K27Ac, "metaInfo")

# Use all the genomic intervals.
estimateSizeFactors(H3K27Ac[4:8])

# Use only the genomic intervals occupied by all the ChIP-seq samples.
estimateSizeFactors(H3K27Ac[4:8], subset = apply(H3K27Ac[9:13], 1, all))

MAnorm2 documentation built on Oct. 29, 2022, 1:12 a.m.