estimateExonGenewisedisp: Estimate Genewise Dispersions from Exon-Level Count Data

In hiraksarkar/edgeR_fork: Empirical Analysis of Digital Gene Expression Data in R

Description

Estimate a dispersion value for each gene from exon-level count data by collapsing exons into the genes to which they belong.

Usage

estimateExonGenewiseDisp(y, geneID, group=NULL)

Arguments

y	either a matrix of exon-level counts or a DGEList object with (at least) elements counts (table of counts summarized at the exon level) and samples (data frame containing information about experimental group, library size and normalization factor for the library size). Each row of y should represent one exon.
geneID	vector of length equal to the number of rows of y, which provides the gene identifier for each exon in y. These identifiers are used to group the relevant exons into genes for the gene-level analysis of splice variation.
group	factor supplying the experimental group/condition to which each sample (column of y) belongs. If NULL (default) the function will try to extract if from y, which only works if y is a DGEList object.

Details

This function can be used to compute genewise dispersion estimates (for an experiment with a one-way, or multiple group, layout) from exon-level count data. estimateCommonDisp and estimateTagwiseDisp are used to do the computation and estimation, and the default arguments for those functions are used.

Value

estimateExonGenewiseDisp returns a vector of genewise dispersion estimates, one for each unique geneID.

Author(s)

Davis McCarthy, Gordon Smyth

See Also

estimateCommonDisp and related functions for estimating the dispersion parameter for the negative binomial model.

Examples

# generate exon counts from NB, create list object
y<-matrix(rnbinom(40,size=1,mu=10),nrow=10)
d<-DGEList(counts=y,group=rep(1:2,each=2))
genes <- rep(c("gene.1","gene.2"), each=5)
estimateExonGenewiseDisp(d, genes)

hiraksarkar/edgeR_fork documentation built on Dec. 20, 2021, 3:52 p.m.