iClusterPlus (version 1.8.0)

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 assigment based on joint inference across data types

Usage

iCluster2(x, K, lambda, method=c("lasso","enet","flasso","glasso","gflasso"), chr=NULL, maxiter=50, eps=1e-4, eps2=1e-8)

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

A list with m elements; each element is a vector with one or two elements depending on the methods used.

method

Method used for clustering and variable selection.

chr

Chromosome labels

maxiter

Maximum iteration for the EM algorithm.

eps

EM algorithm convegence criterion 1.

eps2

EM algorithm convegence criterion 2.

Value

A list with the following elements.

cluster

Cluster assigment.

centers

cluster centers.

Phivec

parameter phi; a vector.

beta

parameter B; a matrix.

meanZ

meanZ

EZZt

EZZt

dif

difference

iter

iteration

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

plotiCluster, compute.pod, iClusterPlus

Examples

## clustering
n1 = 20
n2 = 20
n3 = 20
n = n1+n2+n3
p = 5
q = 100

x = NULL
x1a = matrix(rnorm(n1*p), ncol=p)
x2a = matrix(rnorm(n1*p, -1.5,1), ncol=p)
x3a = matrix(rnorm(n1*p, 1.5, 1), ncol=p)
xa = rbind(x1a,x2a,x3a)
xb = matrix(rnorm(n*q), ncol=q)
x[[1]] = cbind(xa,xb)

x1a = matrix(rnorm(n1*p), ncol=p)
x2a = matrix(rnorm(n1*p, -1.5,1), ncol=p)
x3a = matrix(rnorm(n1*p, 1.5, 1), ncol=p)
xa = rbind(x1a,x2a,x3a)
xb = matrix(rnorm(n*q), ncol=q)
x[[2]] = cbind(xa,xb)

x1a = matrix(rnorm(n1*p), ncol=p)
x2a = matrix(rnorm(n1*p, -1.5,1), ncol=p)
x3a = matrix(rnorm(n1*p, 1.5, 1), ncol=p)
xa = rbind(x1a,x2a,x3a)
xb = matrix(rnorm(n*q), ncol=q)
x[[3]] = cbind(xa,xb)


x1a = matrix(rnorm(n1*p), ncol=p)
x2a = matrix(rnorm(n1*p, -1.5,1), ncol=p)
x3a = matrix(rnorm(n1*p, 1.5, 1), ncol=p)
xa = rbind(x1a,x2a,x3a)
xb = matrix(rnorm(n*q), ncol=q)
x[[4]] = cbind(xa,xb)

x1a = matrix(rnorm(n1*p), ncol=p)
x2a = matrix(rnorm(n1*p, -1.5,1), ncol=p)
x3a = matrix(rnorm(n1*p, 1.5, 1), ncol=p)
xa = rbind(x1a,x2a,x3a)
xb = matrix(rnorm(n*q), ncol=q)
x[[5]] = cbind(xa,xb)

method = c('lasso', 'enet', 'flasso', 'glasso', 'gflasso')  
lambda=alist()
lambda[[1]] = 30
lambda[[2]] = c(20,1)
lambda[[3]] = c(20,20)
lambda[[4]] = 30
lambda[[5]] = c(30,20)

chr=c(rep(1,10),rep(2,(p+q)-10)) 
date()
fit2 = iCluster2(x, K=3, lambda, method=method, chr=chr, maxiter=20,eps=1e-4, eps2=1e-8)
date()

par(mfrow=c(5,1),mar=c(4,4,1,1))
for(i in 1:5){
barplot(fit2$beta[[i]][,1])
}

#library(gplots)
#library(lattice)

#plotHeatmap(fit2, datasets=x, type=rep("gaussian",length(x)), row.order=c(TRUE,TRUE,FALSE,TRUE,FALSE), 
# sparse=rep(FALSE,length(x)), scale=rep("row",5), width=5, col.scheme=rep(list(bluered(256)),length(x)))