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biomvRCNS (version 1.12.0)

Copy Number study and Segmentation for multivariate biological data

Description

In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing.

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Version

Version

1.12.0

License

GPL (>= 2)

Maintainer

Yang Du

Last Published

February 15th, 2017

Functions in biomvRCNS (1.12.0)

biomvRGviz

Plot segmentation result using Gviz
regionSegCost

Regional segmentation cost matrix
maxGapminRun

Max-gap-min-run algorithm for 2 states segmentation
regionSegAlphaNB

Estimate matrix of dispersion parameter alpha (size) used in regionSegCost for negative binomial distributed x.
hsmmRun

Estimating the most likely state sequence using Hidden Semi Markov Model
simSegData

Simulate exemplary segmentation data.
sojournAnno

Estimate sojourn distribution parameters from posterior information like annotation data
encodeTP53

mapped RNA-seq data from ENCODE
biomvRCNS-class

Class "biomvRCNS"
biomvRmgmr

Batch process multiple sequences and samples using max-gap-min-run algorithm for 2 states segmentation
splitFarNeighbour

Split segments if long gaps exist between feature positions
coriell

Array CGH data set of Coriell cell lines
biomvRseg

Homogeneous segmentation of multi-sample genomic data
variosm

Differential methylation data from sequencing
biomvRhsmm

Estimating the most likely state sequence using Hidden Semi Markov Model