iCluster (version 2.1.0)

iCluster2: A variant of the iCluster method with variance weighted shrinkage

Description

iCluster function with variance-weighted shrinkage (see Shen et al. PLoS ONE, 2012)

Usage

iCluster2(datasets, k, lambda=NULL, scale=T, scalar=F, max.iter=10, verbose=T)

Arguments

datasets

A list containing data matrices. For each data matrix, the rows represent samples, and the columns represent genomic features.

k

Number of classes for the samples.

lambda

Penalty term for the coefficient matrix of the iCluster model.

scalar

Logical value. If true, a degenerate version assuming scalar covariance matrix is used.

max.iter

maximum iteration for the EM algorithm

scale

Logical value. If true, data matrix is column centered

verbose

Logical value. If true, print message.

Value

A list with the following elements.

expZ

Latent variable matrix

W

The iCluster model coefficient matrix

PSI

The estimated covariance matrix

clusters

Cluster indicator for samples

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.

Ronglai Shen, Qianxing Mo, Nikolaus Schultz, Venkatraman E. Seshan, Adam B. Olshen, Jason Huse, Marc Ladanyi, Chris Sander. (2012). Integrative Subtype Discovery in Glioblastoma Using iCluster. PLoS ONE 7, e35236

See Also

tune.iCluster2, plotiCluster, compute.pod, plotHeatmap

Examples

library(iCluster)
library(caTools, lib.loc="/apps/Rlib64/")
library(gdata, lib.loc="/apps/Rlib64/")
library(gtools, lib.loc="/apps/Rlib64/")
library(gplots, lib.loc="/apps/Rlib64/")
library(lattice, lib.loc="/apps/Rlib64/")
data(gbm)

#setting the penalty parameter lambda=0 returns non-sparse fit
#fit=iCluster2(datasets=gbm, k=3, lambda=list(0.44,0.33,0.28))

#plotiCluster(fit=fit, label=rownames(gbm[[1]]))

#compute.pod(fit)

#data(coord)
#chr=coord[,1]
#plotHeatmap(fit=fit, data=gbm, feature.order=c(FALSE,TRUE,TRUE),
#sparse=c(FALSE,TRUE,TRUE),plot.chr=c(TRUE,FALSE,FALSE), chr=chr)