Weighted Conditional Log-Likelihood in Terms of Delta
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
weightedCondLogLikDerDelta(y, delta, tag, prior.n=10, ntags=nrow(y[[1]]), der=0)
y |
list with elements comprising the matrices of count data (or pseudocounts) for the different groups |
delta |
delta ( |
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 |
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) |
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.
numeric scalar of function/derivative evaluated for the given gene and delta
Mark Robinson, Davis McCarthy
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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