weightedComLik {edgeR} | R Documentation |
Allow a flexible approach to accounting for a potential dependence of the dispersion on the abundance (expression level) of tags/genes by calculating a weighted 'common' log-likelihood for each gene.
weightedComLik(object,l0,prop.used=0.25) weightedComLikMA(object,l0,prop.used=0.05)
object |
|
l0 |
matrix of the conditional log-likelihood evaluated at a variety of values for the dispersion (on the delta scale, |
prop.used |
scalar giving the proportion of tags/genes in the whole dataset to use in computing the weighted common log-likelihood for each tag/gene. Default value is |
Genes are ordered based on abundance (expression level) and for a given gene, a proportion of the genes close to it are used to compute the common log-likelihood with decreasing weight given to the genes further from the given gene. Weighting is done using the tricube weighting function for weightedComLik
. Computation can be slow relative to other functions in edgeR
, especially if the number of genes or the number of grid values (i.e. the dimensions of l0) are large. weightedComLikMA
uses a moving average to do the weighting (using movingAverageByCol
) and so is much faster than weightedComLik
.
matrix of weighted common log-likelihood values computed for each gene at each grid value for the dispersion. The matrix returned has the same dimensions as l0.
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)) d<-estimateCommonDisp(d) ntags<-nrow(d$counts) y<-splitIntoGroups(new("DGEList",list(counts=d$pseudo.alt,samples=d$samples))) grid.vals<-seq(0.001,0.999,length.out=10) l0<-0 for(i in 1:length(y)) { l0<-condLogLikDerDelta(y[[i]],grid.vals,der=0,doSum=FALSE)+l0 } m0 <- ntags*weightedComLik(d,l0,prop.used=0.25) # Weights sum to 1, so need to multiply by number of tags to give this the same weight overall as the regular common likelihood # Or use the moving-average method m1 <- ntags*weightedComLikMA(d,l0,prop.used=0.05)