Package: PeakSegDisk 2024.10.1

PeakSegDisk: Disk-Based Constrained Change-Point Detection

Disk-based implementation of Functional Pruning Optimal Partitioning with up-down constraints <doi:10.18637/jss.v101.i10> for single-sample peak calling (independently for each sample and genomic problem), can handle huge data sets (10^7 or more).

Authors:Toby Dylan Hocking [aut, cre]

PeakSegDisk_2024.10.1.tar.gz
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PeakSegDisk_2024.10.1.tgz(r-4.6-x86_64)PeakSegDisk_2024.10.1.tgz(r-4.6-arm64)PeakSegDisk_2024.10.1.tgz(r-4.5-x86_64)PeakSegDisk_2024.10.1.tgz(r-4.5-arm64)
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PeakSegDisk_2024.10.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PeakSegDisk/json (API)

# Install 'PeakSegDisk' in R:
install.packages('PeakSegDisk', repos = c('https://tdhock.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tdhock/peaksegdisk/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ChIPreads - Reads aligned to hg19 from two ChIP-seq experiments
  • Mono27ac - A small ChIP-seq data set in which peaks can be found using PeakSegFPOP

On CRAN:

Conda:

cpp

4.67 score 4 stars 39 scripts 757 downloads 10 exports 1 dependencies

Last updated from:7e0e474a16. Checks:11 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR140
linux-devel-x86_64ERROR135
source / vignettesOK223
linux-release-arm64ERROR134
linux-release-x86_64ERROR139
macos-release-arm64ERROR95
macos-release-x86_64ERROR196
macos-oldrel-arm64ERROR86
macos-oldrel-x86_64ERROR219
windows-develERROR137
windows-releaseERROR116
windows-oldrelERROR106
wasm-releaseOK101

Exports:col.name.listfread.firstfread.lastPeakSegFPOP_dfPeakSegFPOP_dirPeakSegFPOP_filePeakSegFPOP_vecsequentialSearch_dirwc2intwriteBedGraph

Dependencies:data.table

Examples
Related work | Simulate a noisy integer vector with changes | Segment a vector of integers | Segment a data frame | Write the file yourself | Computing the model with a given number of peaks
Rendered fromExamples.Rnwusingknitr::knitr

Last update: 2023-11-27
Started: 2019-07-31

Spatial correlation
Rendered fromSpatial_correlation.Rmdusingknitr::knitr

Last update: 2023-09-05
Started: 2019-07-31

Worst case time complexity
Rendered fromWorst_case.Rmdusingknitr::knitr

Last update: 2023-09-05
Started: 2019-07-31