Learn R Programming

jointseg

This package implements functions to quickly segment multivariate signals into piecewise-constant profiles, as well as a framework to generate realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies.

Installation

You can install jointseg from github with:

# install.packages("devtools")
devtools::install_github("mpierrejean/jointseg")

Usage

The main high-level joint segmentation functions are:

  • jointSeg for arbitrary signals, see ?jointSeg.
  • PSSeg for bivariate copy-number signals, see ?PSSeg and vignette("PSSeg").

We also refer to vignette("dataGeneration") for a description of the generation of synthetic DNA copy-number profiles using data from the acnr package.

References

Pierre-Jean, M, Rigaill, G. J. and Neuvial, P. (2015). "Performance Evaluation of DNA Copy Number Segmentation Methods." Briefings in Bioinformatics, no. 4: 600–615.

Software status

Resource:GitHubTravis CIAppveyor
Platforms:MultipleLinux & OS XWindows
R CMD check
Test coverage

Copy Link

Version

Install

install.packages('jointseg')

Monthly Downloads

344

Version

1.0.2

License

LGPL (>= 2.1)

Issues

Pull Requests

Stars

Forks

Last Published

January 11th, 2019

Functions in jointseg (1.0.2)

getCopyNumberDataByResampling

Generate a copy number profile by resampling
getTpFp

Calculate the number of true positives and false positives
doRBS

Run RBS segmentation
randomProfile

Generate a random multi-dimensional profile with breakpoints and noise
retour_sn

Extract endpoint matrix from DP result
estimateSd

Robust standard deviation estimator
PSSeg

Parent-Specific copy number segmentation
oneBkp

Get best candidate change point
getUnivStat

Get the binary test statistic for one dimension
getUnivJ

Get the contribution of one dimension to the RSE.
plotSeg

Plot signal and breakpoints with segment-level signal estimates
prof

profile time and memory usage of a given R expression
pruneByDP

Exact segmentation of a multivariate signal using dynamic programming.
modelSelection

Model selection
multiplyXtXBySparse

multiplyXtXBySparse
mapPositionsBack

mapPositionsBack
leftMultiplyByXt

leftMultiplyByXt
jointSeg

Joint segmentation of multivariate signals
segmentByGFLars

Group fused Lars segmentation (low-level)
leftMultiplyByInvXAtXA

leftMultiplyByInvXAtXA
segmentByRBS

Recursive Binary Segmentation (low-level)
doPSCBS

Run Paired PSCBS segmentation
defaultWeights

Compute default weights for the weighted group fused Lasso
doCBS

Run CBS segmentation
doPSCN

Run PSCN segmentation (defunct)
anotherBkp

Get best candidate change point
binMissingValues

binMissingValues
doGFLars

Group fused Lars segmentation
doDynamicProgramming

Run segmentation by dynamic programming
Fpsn

Pruned dynamic programming algorithm