PeakSegJoint (version 2022.4.6)

PeakSegJointFaster: PeakSegJointFaster

Description

Run the PeakSegJointFaster heuristic optimization algorithm, for several bin.factor parameter values, keeping only the most likely model found. This gives an approximate solution to a multi-sample Poisson maximum likelihood segmentation problem. Given S samples, this function computes a sequence of S+1 PeakSegJoint models, with 0, ..., S samples with an overlapping peak (maximum of one peak per sample). It also computes for G groups, the seq of G+1 models, with 0, ..., G groups with an overlapping peak.

Usage

PeakSegJointFaster(profiles, 
    bin.factor.vec = 2:7)

Arguments

profiles

data.frame with columns sample.id, sample.group, chromStart, chromEnd, count.

bin.factor.vec

Size of bin pyramid. Bigger values result in slower computation.

Value

List of model fit results.

Examples

# NOT RUN {
library(PeakSegJoint)
data(H3K36me3.TDH.other.chunk1, envir=environment())
some.counts <- subset(
  H3K36me3.TDH.other.chunk1$counts,
  43000000 < chromEnd &
  chromStart < 43200000)
some.counts$sample.group <- some.counts$cell.type

fit <- PeakSegJointFaster(some.counts, 2:7)

if(interactive() && require(ggplot2)){

  both <- with(fit, rbind(
    data.frame(model="sample", sample.modelSelection),
  data.frame(model="group", group.modelSelection)))
  ggplot()+
    ggtitle("model selection functions")+
    scale_size_manual(values=c(sample=2, group=1))+
    geom_segment(aes(min.log.lambda, complexity,
                     color=model, size=model,
                     xend=max.log.lambda, yend=complexity),
                 data=both)+
    xlab("log(penalty)")+
    ylab("model complexity (samples or groups with a common peak)")

}

# }