tune.iCluster2 {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 assignment based on joint inference across data types

### Usage

    tune.iCluster2(x, K, method=c("lasso","enet","flasso","glasso","gflasso"),base=200,
      chr=NULL,true.class=NULL,lambda=NULL,n.lambda=NULL,save.nonsparse=F,nrep=10,eps=1e-4)

### Arguments

`x` | 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` | User supplied matrix of lambda to tune.
`method` | Method used for clustering and variable selection.
`chr` | Chromosome labels
`n.lambda` | Number of lambda to sample using uniform design.
`nrep` | Fold of cross-validation.
`base` | Base.
`true.class` | True class label if available.
`save.nonsparse` | Logic argument whether to save the nonsparse fit.
`eps` | EM algorithm convergence criterion

### Value

A list with the following elements.

`best.fit` | Best fit.
`best.lambda` | Best lambda.
`ps` | Rand index
`ps.adjusted` | Adjusted Rand index.

### Author(s)

Qianxing Mo qianxing.mo@moffitt.org,Ronglai Shen,Sijian Wang

### References

Ronglai Shen, Sijian Wang, Qianxing Mo. (2013). Sparse Integrative Clustering of Multiple Omics Data Sets. Annals of Applied Statistics. 7(1):269-294

### See Also

`iCluster2`

[Package iClusterPlus version 1.30.0 Index]