Weighted Conditional Log-Likelihood in Terms of Delta

Description

Weighted conditional log-likelihood parameterized in terms of delta (phi / (phi+1)) for a given gene, maximized to find the smoothed (moderated) estimate of the dispersion parameter

Usage

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weightedCondLogLikDerDelta(y, delta, tag, prior.n=10, ntags=nrow(y[[1]]), der=0)

Arguments

y

list with elements comprising the matrices of count data (or pseudocounts) for the different groups

delta

delta (phi / (phi+1))parameter of negative binomial

tag

gene at which the weighted conditional log-likelihood is evaluated

prior.n

smoothing paramter that indicates the weight to put on the common likelihood compared to the individual gene's likelihood; default 10 means that the common likelihood is given 10 times the weight of the individual gene's likelihood in the estimation of the genewise dispersion

ntags

numeric scalar number of genes in the dataset to be analysed

der

derivative, either 0 (the function), 1 (first derivative) or 2 (second derivative)

Details

This function computes the weighted conditional log-likelihood for a given gene, parameterized in terms of delta. The value of delta that maximizes the weighted conditional log-likelihood is converted back to the phi scale, and this value is the estimate of the smoothed (moderated) dispersion parameter for that particular gene. The delta scale for convenience (delta is bounded between 0 and 1). Users should note that ‘tag’ and ‘gene’ are synonymous when interpreting the names of the arguments for this function.

Value

numeric scalar of function/derivative evaluated for the given gene and delta

Author(s)

Mark Robinson, Davis McCarthy

Examples

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counts<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=counts,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
y<-splitIntoGroups(d)
ll1<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=0)
ll2<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=1)

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