iClusterPlus (version 1.8.0)

tune.iClusterPlus: Integrative clustering of multiple genomic data

Description

Given multiple genomic data (e.g., copy number, gene expression, DNA methylation) measured in the same set of samples, tune.iClusterPlus uses a series of lambda values to fit a regularized latent variable model based clustering that generates an integrated cluster assignment based on joint inference across data.

Usage

tune.iClusterPlus(cpus=8,dt1,dt2=NULL,dt3=NULL,dt4=NULL, type=c("gaussian","binomial","poisson","multinomial"), K=2,alpha=c(1,1,1,1),n.lambda=NULL,scale.lambda=c(1,1,1,1), n.burnin=200,n.draw=200,maxiter=20,sdev=0.05,eps=1.0e-4)

Arguments

cpus

Number of CPU used for parallel computation.

dt1

A data matrix. The rows represent samples, and the columns represent genomic features.

dt2

A data matrix. The rows represent samples, and the columns represent genomic features.

dt3

A data matrix. The rows represent samples, and the columns represent genomic features.

dt4

A data matrix. The rows represent samples, and the columns represent genomic features.

type

data type, which can be "gaussian","binomial","poisson", and"multinomial".

K

The number of eigen features. Given K, the number of cluster is K+1.

alpha

Vector of elasticnet penalty terms. At this version of iClusterPlus, elasticnet is not used. Therefore, all the elements of alpha are set to 1.

n.lambda

Number of lambda are tuned.

scale.lambda

A value between (0,1); the actual lambda values will be scale.lambda multiplying the lambda values of the uniform design.

n.burnin

Number of MCMC burnin.

n.draw

Number of MCMC draw.

maxiter

Maximum iteration for the EM algorithm.

sdev

standard deviation of random walk proposal.

eps

EM algorithm convergence criterion.

Value

A list with the two elements 'fit' and 'lambda', where fit itself is a list and lambda is a matrix. Each row of lambda is the lambda values used to fit iClusterPlus model. Each component of fit is an object return by iClusterPlus, one-to-one corresponding to the row of lambda. Each component of fit has the following objects.

alpha

Intercept parameter for the genomic features.

beta

Information parameter for the genomic features. The rows and the columns represent the genomic features and the coefficients for the latent variable, respectively.

clusters

Cluster assignment.

centers

Cluster centers.

meanZ

Latent variable.

References

Qianxing Mo, Sijian Wang, Venkatraman E. Seshan, Adam B. Olshen, Nikolaus Schultz, Chris Sander, R. Scott Powers, Marc Ladanyi, and Ronglai Shen. (2012). Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl. Acad. Sci. USA 110(11):4245-50.

See Also

plotiCluster,iClusterPlus,iCluster2,iCluster, compute.pod

Examples

### see the users' guide iManul.pdf