Inference: Postprocessing - Posterior Inference

In iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data

Description

Performs posterior inference on the output of the main function

Usage

Inference(listComplete, G, M, niter, burnin, threshold = 0.5)

Arguments

listComplete	Output of the main function
G	Number of gene expression probes
M	Number of CGH probes
niter	Number of Monte Carlo Markov Chain iterations
burnin	Burn-in
threshold	Threshold on the posterior probabilities of inclusion of the association matrix

Details

Wrapper function which calls InferenceR, InferenceXi, InferenceA, InferenceMu, InferenceSd on the output of the main function.

Value

A list made by the following items

R	Binary matrix of estimated association
Xi	Matrix of estimated copy number states
A	Estimated transition matrix
Mu	Estimated vector of state specific means
Sd	Estimated vector of state specific standard deviations

Author(s)

Alberto Cassese

References

Cassese A, Guindani M, Tadesse M, Falciani F, Vannucci M. A hierarchical Bayesian model for inference of copy number variants and their association to gene expression. Annals of Applied Statistics, 8(1), 148-175.
Cassese A, Guindani M, Vannucci M. A Bayesian integrative model for genetical genomics with spatially informed variable selection. Cancer Informatics.

See Also

See Also InferenceR,InferenceXi

Examples

## Not run: 
data(NCI_60)

Y <- NCI_60$Affy
X <- NCI_60$aCGH
distance <- NCI_60$distance
disfix <- 146274826
xi <- InitXi(X)
tran <- Tran(xi)
mu <- InitMu()
d=0.2587288

Y <- Center(Y)

res <- iBAT(Y=Y,X=X,distance=distance,disfix=disfix,xi=xi,tran=tran,mu=mu,d=d)

summRes <- Inference(res,G=dim(Y)[[2]],M=dim(X)[[2]],niter=niter,burnin=bi,threshold=0.5)

## End(Not run)

iBATCGH documentation built on Oct. 23, 2020, 6:34 p.m.