PeakSegOptimal (version 2024.10.1)

PeakSegPDPAInf: PeakSegPDPAInf

Description

Find the optimal change-points using the Poisson loss and the PeakSeg constraint. This function is an interface to the C++ code which always uses -Inf for the first interval's lower limit and Inf for the last interval's upper limit -- it is for testing the number of intervals between the two implementations.

Usage

PeakSegPDPAInf(count.vec, 
    weight.vec = rep(1, 
        length(count.vec)), 
    max.segments = NULL)

Value

List of model parameters. count.vec, weight.vec, n.data, max.segments (input parameters), cost.mat (optimal Poisson loss), ends.mat (optimal position of segment ends, 1-indexed), mean.mat (optimal segment means), intervals.mat (number of intervals stored by the functional pruning algorithm). To recover the solution in terms of (M,C) variables, see the example.

Arguments

count.vec

integer vector of count data.

weight.vec

numeric vector (same length as count.vec) of positive weights.

max.segments

integer of length 1: maximum number of segments (must be >= 2).

Author

Toby Dylan Hocking <[e-mail]> [aut, cre]

Examples

## Use the algo to compute the solution list.
library(PeakSegOptimal)
data("H3K4me3_XJ_immune_chunk1", envir=environment())
by.sample <-
  split(H3K4me3_XJ_immune_chunk1, H3K4me3_XJ_immune_chunk1$sample.id)
n.data.vec <- sapply(by.sample, nrow)
one <- by.sample[[1]]
count.vec <- one$coverage
weight.vec <- with(one, chromEnd-chromStart)
max.segments <- 19L

library(data.table)
ic.list <- list()
for(fun.name in c("PeakSegPDPA", "PeakSegPDPAInf")){
  fun <- get(fun.name)
  fit <- fun(count.vec, weight.vec, max.segments)
  ic.list[[fun.name]] <- data.table(
    fun.name,
    segments=as.numeric(row(fit$intervals.mat)),
    data=as.numeric(col(fit$intervals.mat)),
    cost=as.numeric(fit$cost.mat),
    intervals=as.numeric(fit$intervals.mat))
}
ic <- do.call(rbind, ic.list)[0 < intervals]
intervals <- dcast(ic, data + segments ~ fun.name, value.var="intervals")
cost <- dcast(ic, data + segments ~ fun.name, value.var="cost")
not.equal <- cost[PeakSegPDPA != PeakSegPDPAInf]
stopifnot(nrow(not.equal)==0)

intervals[, increase := PeakSegPDPAInf-PeakSegPDPA]
table(intervals$increase)
quantile(intervals$increase)
ic[, list(
  mean=mean(intervals),
  max=max(intervals)
  ), by=list(fun.name)]