R: A variant of the iCluster method with variance weighted...

iCluster2 {iCluster} | R Documentation

## 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

### Author(s)

Ronglai Shen shenr@mskcc.org

### 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)

[Package iCluster version 2.1.0 Index]