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RJaCGH (version 2.0.3)

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 common regions for gains/losses.

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Version

Install

install.packages('RJaCGH')

Monthly Downloads

102

Version

2.0.3

License

GPL-3

Maintainer

Oscar Rueda

Last Published

December 11th, 2014

Functions in RJaCGH (2.0.3)

pREC_S

Subgroups of arrays that share common alterations
RJaCGH

Reversible Jump MCMC for the analysis of arrays of CGH
pREC_A

Probabilistic Common Regions for copy number alteration.
genomePlot

Plot of the genome with probabilities of alteration.
modelAveraging

Method for model averaging for RJaCGH objects.
normal.HMM.likelihood.NH.C

Likelihood for non-homogeneous hidden Markov model
trace.plot

Trace plot for 'RJaCGH' object
plot.pREC_S

Plot number of probes shared by pairs of arrays
states

'states' method for RJaCGH objects
print.pREC_S

Method for printing probabilistic common regions
summary.RJaCGH

Summarizing RJaCGH models
smoothMeans

Smoothed posterior mean
print.summary.RJaCGH

print summary of RJaCGH fit
plot.Q.NH

Plot transition probabilities
simulateRJaCGH

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

Transition Matrix for non-homogeneous Hidden Markov Model
print.pREC_A

Method for printing probabilistic common region.
snijders

Public CGH data of Snijders
plot.RJaCGH

'plot' method for RJaCGH objects
relabelStates

Relabelling of hidden states to biological states of alteration.