Postprocessing - Posterior Inference

Share:

Description

Performs posterior inference on the output of the main function

Usage

1
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
## 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)