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RJaCGH (version 1.2.5)

Reversible Jump MCMC for the analysis of CGH arrays.

Description

Bayesian analysis of CGH microarrays fitting Hidden Markov Chain models. The selection of the number of states is made via their posterior probability computed by Reversible Jump Markov Chain Monte Carlo Methods. Also returns probabilistic minimal common regions for gains/losses.

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Version

Install

install.packages('RJaCGH')

Monthly Downloads

102

Version

1.2.5

License

GPL (>=2)

Maintainer

Oscar Rueda

Last Published

February 15th, 2017

Functions in RJaCGH (1.2.5)

Joint posterior probabilities of alteration

'plot' method for RJaCGH objects

Plot of the genome with probabilities of alteration.

Viterbi path from every MCMC sample

print.summary.RJaCGH

print summary of RJaCGH fit

Plot transition probabilities

model.averaging

Method for model averaging for RJaCGH objects.

Summarizing RJaCGH models

plot.pREC_S.RJaCGH.array

Plot number of probes shared by pairs of arrays

Public CGH data of Snijders

Select between several parallel chains ('RJaCGH' objects)

Probabilistic Common Regions for copy number alteration.

Simulate observations form a hidden Markov model with non-homogeneous transition probabilities.

Subgroups of arrays that share common alterations

print.pREC_S.RJaCGH.array

Method for printing probabilistic common regions

Collapse several parallel chains ('RJaCGH' objects)

'states' method for RJaCGH objects

Transition Matrix for non-homogeneous Hidden Markov Model

Trace plot for 'RJaCGH' object

Reversible Jump MCMC for the analysis of arrays of CGH

Relabelling of hidden states to biological states of alteration.

normal.HMM.likelihood.NH.C

Likelihood for non-homogeneous hidden Markov model

Smoothed posterior mean

gelman.brooks.plot

gelman-brooks plot for 'RJaCGH' objects

print.pREC_A.RJaCGH

Method for printing probabilistic minimal common region.