iClusterPlus (version 1.8.0)

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.

meanZ

Relaxed cluster indicator matrix.

beta

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)

#library(gplots)
#library(lattice)
#col.scheme = alist()
#col.scheme[[1]] = bluered(256)
#col.scheme[[2]] = greenred(256)
#cn.image=breast.chr17[[2]]
#cn.image[cn.image>1.5]=1.5
#cn.image[cn.image< -1.5]= -1.5
#exp.image=breast.chr17[[1]]
#exp.image[exp.image>3]=3
#exp.image[exp.image< -3]=3
#plotHeatmap(fit, datasets=list(cn.image,exp.image), type=c("gaussian","gaussian"),
#  row.order=c(FALSE,FALSE), width=5, col.scheme=col.scheme)