getPriorN: Get a Recommended Value for Prior N from DGEList Object

Description

Returns the lib.size component of the samples component of DGEList object multiplied by the norm.factors component

Usage

getPriorN(y, design=NULL, prior.df=20)

Arguments

a DGEList object with (at least) elements counts (table of unadjusted counts) and samples (data frame containing information about experimental group, library size and normalization factor for the library size)

design

numeric matrix (optional argument) giving the design matrix for the GLM that is to be fit. Must be of full column rank. If provided design is used to determine the number of parameters to be fit in the statistical model and therefore the residual degrees of freedom. If left as the default (NULL) then the y$samples$group element of the DGEList object is used to determine the residual degrees of freedom.

prior.df

numeric scalar giving the weight, in terms of prior degrees of freedom, to be given to the common parameter likelihood when estimating genewise dispersion estimates.

Value

getPriorN returns a numeric scalar

Details

When estimating genewise dispersion values using estimateTagwiseDisp or estimateGLMTagwiseDisp we need to decide how much weight to give to the common parameter likelihood in order to smooth (or stabilize) the dispersion estimates. The best choice of value for the prior.n parameter varies between datasets depending on the number of samples in the dataset and the complexity of the model to be fit. The value of prior.n should be inversely proportional to the residual degrees of freedom. We have found that choosing a value for prior.n that is equivalent to giving the common parameter likelihood 20 degrees of freedom generally gives a good amount of smoothing for the genewise dispersion estimates. This function simply recommends an appropriate value for prior.n---to be used as an argument for estimateTagwiseDisp or estimateGLMTagwiseDisp---given the experimental design at hand and the chosen prior degrees of freedom.

Examples

Run this code

# generate raw counts from NB, create list object
y<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
getPriorN(d)