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RJaCGH (version 2.0.4)

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

2.0.4

License

GPL-3

Maintainer

Oscar Rueda

Last Published

July 10th, 2015

Functions in RJaCGH (2.0.4)

genomePlot

Plot of the genome with probabilities of alteration.
print.summary.RJaCGH

print summary of RJaCGH fit
smoothMeans

Smoothed posterior mean
simulateRJaCGH

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

Trace plot for 'RJaCGH' object
plotQNH

Plot transition probabilities
plot.RJaCGH

'plot' method for RJaCGH objects
modelAveraging

Method for model averaging for RJaCGH objects.
relabelStates

Relabelling of hidden states to biological states of alteration.
states

'states' method for RJaCGH objects
print.pREC_A

Method for printing probabilistic common region.
normal.HMM.likelihood.NH.C

Likelihood for non-homogeneous hidden Markov model
summary.RJaCGH

Summarizing RJaCGH models
RJaCGH

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

Method for printing probabilistic common regions
snijders

Public CGH data of Snijders
pREC_S

Subgroups of arrays that share common alterations
plot.pREC_S

Plot number of probes shared by pairs of arrays
QNH

Transition Matrix for non-homogeneous Hidden Markov Model
pREC_A

Probabilistic Common Regions for copy number alteration.