iCluster (version 2.1.0)

iCluster: Integrative clustering of multiple genomic data types

Description

Given multiple genomic data types (e.g., copy number, gene expression, DNA methylation) measured in the same set of samples, iCluster fits a regularized latent variable model based clustering that generates an integrated cluster assigment based on joint inference across data types

Usage

iCluster(datasets, k, lambda, scalar=FALSE, max.iter=50,epsilon=1e-3)

Arguments

datasets

A list object containing m data matrices representing m different genomic data types measured in a set of n samples. For each matrix, the rows represent samples, and the columns represent genomic features.

k

Number of subtypes.

lambda

Vector of length-m lasso penalty terms.

scalar

If TRUE, assumes scalar covariance matrix Psi. Default is FALSE.

max.iter

Maximum iteration for the EM algorithm.

epsilon

EM algorithm convegence criterion.

Value

A list with the following elements.

expZ

Relaxed cluster indicator matrix.

W

Coefficient matrix.

clusters

Cluster assigment.

conv.rate

Convergence history.

References

Ronglai Shen, Adam Olshen, Marc Ladanyi. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912.

See Also

breast.chr17,plotiCluster, compute.pod

Examples

data(breast.chr17)
fit=iCluster(breast.chr17, k=4, lambda=c(0.2,0.2))
plotiCluster(fit=fit, label=rownames(breast.chr17[[2]]))
compute.pod(fit)