iCluster2: A variant of the iCluster method with variance weighted...

In iCluster: Integrative clustering of multiple genomic data types

Description

iCluster function with variance-weighted shrinkage (see Shen et al. PLoS ONE, 2012)

Usage

iCluster2(datasets, k, lambda=NULL, scale=T, scalar=F, max.iter=10, verbose=T)

Arguments

datasets	A list containing data matrices. For each data matrix, the rows represent samples, and the columns represent genomic features.
k	Number of classes for the samples.
lambda	Penalty term for the coefficient matrix of the iCluster model.
scalar	Logical value. If true, a degenerate version assuming scalar covariance matrix is used.
max.iter	maximum iteration for the EM algorithm
scale	Logical value. If true, data matrix is column centered
verbose	Logical value. If true, print message.

Value

A list with the following elements.

expZ	Latent variable matrix
W	The iCluster model coefficient matrix
PSI	The estimated covariance matrix
clusters	Cluster indicator for samples

Author(s)

Ronglai Shen [e-mail]

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.

Ronglai Shen, Qianxing Mo, Nikolaus Schultz, Venkatraman E. Seshan, Adam B. Olshen, Jason Huse, Marc Ladanyi, Chris Sander. (2012). Integrative Subtype Discovery in Glioblastoma Using iCluster. PLoS ONE 7, e35236

See Also

tune.iCluster2, plotiCluster, compute.pod, plotHeatmap

Examples

library(iCluster)
library(caTools, lib.loc="/apps/Rlib64/")
library(gdata, lib.loc="/apps/Rlib64/")
library(gtools, lib.loc="/apps/Rlib64/")
library(gplots, lib.loc="/apps/Rlib64/")
library(lattice, lib.loc="/apps/Rlib64/")
data(gbm)

#setting the penalty parameter lambda=0 returns non-sparse fit
#fit=iCluster2(datasets=gbm, k=3, lambda=list(0.44,0.33,0.28))

#plotiCluster(fit=fit, label=rownames(gbm[[1]]))

#compute.pod(fit)

#data(coord)
#chr=coord[,1]
#plotHeatmap(fit=fit, data=gbm, feature.order=c(FALSE,TRUE,TRUE),
#sparse=c(FALSE,TRUE,TRUE),plot.chr=c(TRUE,FALSE,FALSE), chr=chr)

Example output

Loading required package: lattice
Loading required package: caTools
Loading required package: gdata
sh: 1: cannot create /dev/null: Permission denied
gdata: Unable to locate valid perl interpreter
gdata: 
gdata: read.xls() will be unable to read Excel XLS and XLSX files
gdata: unless the 'perl=' argument is used to specify the location of a
gdata: valid perl intrpreter.
gdata: 
gdata: (To avoid display of this message in the future, please ensure
gdata: perl is installed and available on the executable search path.)
sh: 1: cannot create /dev/null: Permission denied
gdata: Unable to load perl libaries needed by read.xls()
gdata: to support 'XLX' (Excel 97-2004) files.

gdata: Unable to load perl libaries needed by read.xls()
gdata: to support 'XLSX' (Excel 2007+) files.

gdata: Run the function 'installXLSXsupport()'
gdata: to automatically download and install the perl
gdata: libaries needed to support Excel XLS and XLSX formats.

Attaching package: 'gdata'

The following object is masked from 'package:stats':

    nobs

The following object is masked from 'package:utils':

    object.size

The following object is masked from 'package:base':

    startsWith

Loading required package: gtools
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: parallel

iCluster documentation built on May 2, 2019, 11:25 a.m.