Joint Segmentation of Multivariate (Copy Number) Signals
Get best candidate change point
binMissingValues
Compute default weights for the weighted group fused Lasso
Run CBS segmentation
Run segmentation by dynamic programming
Group fused Lars segmentation
Run Paired PSCBS segmentation
Run PSCN segmentation (defunct)
Run RBS segmentation
Robust standard deviation estimator
Pruned dynamic programming algorithm
Generate a copy number profile by resampling
Calculate the number of true positives and false positives
Get the contribution of one dimension to the RSE.
Get the binary test statistic for one dimension
Joint segmentation of multivariate signals
leftMultiplyByInvXAtXA
leftMultiplyByXt
mapPositionsBack
Model selection
multiplyXtXBySparse
Get best candidate change point
Plot signal and breakpoints with segment-level signal estimates
profile time and memory usage of a given R expression
Exact segmentation of a multivariate signal using dynamic programming.
Parent-Specific copy number segmentation
Generate a random multi-dimensional profile with breakpoints and noise
Extract endpoint matrix from DP result
Group fused Lars segmentation (low-level)
Recursive Binary Segmentation (low-level)
Methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating 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. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.