iCluster {iClusterPlus} | R Documentation

## 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.

### 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.

### 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)

[Package iClusterPlus version 1.22.0 Index]