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RJaCGH (version 1.5.7)

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

1.5.7

License

GPL-3

Maintainer

Oscar Rueda

Last Published

February 15th, 2017

Functions in RJaCGH (1.5.7)

model.averaging

Method for model averaging for RJaCGH objects.
simulateRJaCGH

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

print summary of RJaCGH fit
collapseChain

Collapse several parallel chains ('RJaCGH' objects)
normal.HMM.likelihood.NH.C

Likelihood for non-homogeneous hidden Markov model
summary.RJaCGH

Summarizing RJaCGH models
pREC_A

Probabilistic Common Regions for copy number alteration.
smoothMeans

Smoothed posterior mean
Q.NH

Transition Matrix for non-homogeneous Hidden Markov Model
gelman.rubin.plot

gelman-rubin plot for 'RJaCGH' objects
chainsSelect

Select between several parallel chains ('RJaCGH' objects)
states

'states' method for RJaCGH objects
plot.RJaCGH

'plot' method for RJaCGH objects
RJaCGH

Reversible Jump MCMC for the analysis of arrays of CGH
print.pREC_S

Method for printing probabilistic common regions
pREC_S

Subgroups of arrays that share common alterations
relabelStates

Relabelling of hidden states to biological states of alteration.
genome.plot

Plot of the genome with probabilities of alteration.
plot.pREC_S

Plot number of probes shared by pairs of arrays
snijders

Public CGH data of Snijders
print.pREC_A

Method for printing probabilistic common region.
trace.plot

Trace plot for 'RJaCGH' object
plot.Q.NH

Plot transition probabilities