tune.iCluster2: Integrative clustering of multiple genomic data types

In iClusterPlus: Integrative clustering of multi-type genomic data

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 [e-mail],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

iClusterPlus documentation built on Nov. 8, 2020, 8:01 p.m.