iClusterPlus (version 1.8.0)

tune.iCluster2: 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.

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