eventTimingOverList: eventTiming for multiple samples and regions

Description

eventTimingOverList is a wrapper to eventTiming that allows for timing of common events over several regions of a sample and/or multiple samples. getPi0Summary gets summary information about $\pi_0$ (the first event) from the output of that function and returns a simple dataframe of the estimate of $\pi_0$ for every region and sample combination.

Usage

eventTimingOverList(dfList, normCont, eventArgs)
getPi0Summary(eventList, CI = TRUE)

Arguments

dfList

a list of mutation data per sample. Each element of the list corresponds to a sample, and consists of a data frame of mutation information for all of the regions that are to be timed. The data frame must have a certain format, see description below.

normCont

a vector of equal length as dfList giving the normal contamination of each sample

eventArgs

list of arguments passed to eventTiming via `do.call'. Should NOT contain the arguments `x',`m',`history',`totalCopy',`type',`mutationId' or `normCont'

eventList

Output of eventTimingOverList (see below)

logical, whether to grab CI from the output.

Value

eventTimingOverList returns a list equal to the number of samples. Each element of that list (i.e. per sample) is itself a list of length three corresponding to the three types of regions that can be timed, ``CNLOH",``SingleGain", and ``DoubleGain". Each of these gives the output of eventTiming per region of that type.

Details

The data frame of mutation data for each sample must have the following columns: `segId', `type', `nMutAllele', `nReads', `mutationId'. The rows of the data frame correspond to mutated locations within the sample. `segId' is an identifier of the segmented region that the mutation is in; eventTiming will be run using the mutation data for each `segId' in the sample. `type' identifies the type of segment, one of c("Other","CNLOH","SingleGain","Diploid","DoubleGain"). `nMutAllele' gives for every row (mutated location) the number of reads with the mutation and corresponds to `x' imputed into eventTiming. `nReads' gives the total number of reads covering the location and corresponds to `m' imputed into eventTiming. `mutationId' is a unique identifier for the mutation.

For arguments passed to eventTiming, it is recommended to pass verbose=FALSE if you want to avoid many warnings about mutations that do not meet the necessary criteria of coverage, etc.

Examples

Run this code

if(require(GenomicRanges)){
	#fix up mutation data to right format
	data(mutData)
	colnames(mutData)[1]<-c("chr")
	colnames(mutData)[grep("t_alt_count",colnames(mutData))]<-"nMutAllele"
	colnames(mutData)[grep("t_ref_count",colnames(mutData))]<-"nRefAllele"
	mutData$nReads<-mutData$nMutAllele+mutData$nRefAllele
	mutData$mutationId<-1:nrow(mutData)
	
	#add segmentation annotation -- second region is bogus, only for illustration
	segs<-data.frame(chr=c(17,17),start=c(1,1.8e7+100),end=c(1.8e7,81195210),
		normCont=0.22,segId=c("Seg1","Seg2"),type=c("CNLOH","SingleGain"))
	##Create Trivial segmentation annotation for example
	mutId<-mut2Seg(mutData,segs)
	eventOut<-eventTimingOverList(dfList=list(Sample1=mutId),normCont=0.22)
	getPi0Summary(eventOut)
}