PeakSegDisk (version 2018.11.28)

problem.sequentialSearch: problem sequentialSearch

Description

Compute the most likely peak model with at most the number of

peaks given by peaks.int. This function repeated calls

problem.PeakSegFPOP with different penalty values, until either

(1) it finds the peaks.int model, or (2) it concludes that there

is no peaks.int model, in which case it returns the next simplest

model (with fewer peaks than peaks.int).

Usage

problem.sequentialSearch(problem.dir, peaks.int, verbose = 0)

Arguments

problem.dir

problemID directory in which coverage.bedGraph has already been

computed. If there is a labels.bed file then the number of

incorrect labels will be computed in order to find the target

interval of minimal error penalty values.

peaks.int

int: target number of peaks.

verbose

Print messages?

Value

Same result list from problem.PeakSegFPOP, with an additional

component "others" describing the other models that were computed

before finding the optimal model with peaks.int (or fewer)

peaks. Additional loss columns are as follows: under=number of

peaks in smaller model during binary search; over=number of peaks

in larger model during binary search; iteration=number of times

PeakSegFPOP has been run.

Examples

# NOT RUN {




## Create simple 6 point data set discussed in supplementary


## materials. GFPOP/GPDPA computes up-down model with 2 peaks, but


## neither CDPA (PeakSegDP::cDPA) nor PDPA (jointseg)


r <- function(chrom, chromStart, chromEnd, coverage){


  data.frame(chrom, chromStart, chromEnd, coverage)


}


supp <- rbind(


  r("chr1", 0, 1,  3),


  r("chr1", 1, 2, 9),


  r("chr1", 2, 3, 18),


  r("chr1", 3, 4, 15),


  r("chr1", 4, 5, 20),


  r("chr1", 5, 6, 2)


)


data.dir <- file.path(tempfile(), "chr1-0-6")


dir.create(data.dir, recursive=TRUE)


write.table(


  supp, file.path(data.dir, "coverage.bedGraph"),


  sep="\t", row.names=FALSE, col.names=FALSE)





## Compute optimal up-down model with 2 peaks via sequential search.


fit <- PeakSegDisk::problem.sequentialSearch(data.dir, 2L)





library(ggplot2)


ggplot()+


  theme_bw()+


  geom_point(aes(


    chromEnd, coverage),


    data=supp)+


  geom_segment(aes(


    chromStart+0.5, mean,


    xend=chromEnd+0.5, yend=mean),


    data=fit$segments,


    color="green")





# }