R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Integrative clustering of multiple genomic data types

iCluster

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 assigment based on joint inference across data types

Usage

iCluster(datasets, k, lambda, scalar=FALSE, max.iter=50,epsilon=1e-3)

Arguments

`datasets`	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`	Vector of length-m lasso penalty terms.
`scalar`	If TRUE, assumes scalar covariance matrix Psi. Default is FALSE.
`max.iter`	Maximum iteration for the EM algorithm.
`epsilon`	EM algorithm convegence criterion.

Value

A list with the following elements.

`meanZ`	Relaxed cluster indicator matrix.
`beta`	Coefficient matrix.
`clusters`	Cluster assigment.
`conv.rate`	Convergence history.

Author(s)

Ronglai Shen shenr@mskcc.org

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.

Examples


data(breast.chr17)
fit=iCluster(breast.chr17, k=4, lambda=c(0.2,0.2))
plotiCluster(fit=fit, label=rownames(breast.chr17[[2]]))
compute.pod(fit)

#library(gplots)
#library(lattice)
#col.scheme = alist()
#col.scheme[[1]] = bluered(256)
#col.scheme[[2]] = greenred(256)
#cn.image=breast.chr17[[2]]
#cn.image[cn.image>1.5]=1.5
#cn.image[cn.image< -1.5]= -1.5
#exp.image=breast.chr17[[1]]
#exp.image[exp.image>3]=3
#exp.image[exp.image< -3]=3
#plotHeatmap(fit, datasets=list(cn.image,exp.image), type=c("gaussian","gaussian"),
#  row.order=c(FALSE,FALSE), width=5, col.scheme=col.scheme)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(iClusterPlus)
Loading required package: parallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/iClusterPlus/iCluster.Rd_%03d_medium.png", width=480, height=480)
> ### Name: iCluster
> ### Title: Integrative clustering of multiple genomic data types
> ### Aliases: iCluster
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> data(breast.chr17)
> fit=iCluster(breast.chr17, k=4, lambda=c(0.2,0.2))
K=4:1234567891011121314151617181920212223> plotiCluster(fit=fit, label=rownames(breast.chr17[[2]]))
> compute.pod(fit)
[1] 0.1519533
> 
> #library(gplots)
> #library(lattice)
> #col.scheme = alist()
> #col.scheme[[1]] = bluered(256)
> #col.scheme[[2]] = greenred(256)
> #cn.image=breast.chr17[[2]]
> #cn.image[cn.image>1.5]=1.5
> #cn.image[cn.image< -1.5]= -1.5
> #exp.image=breast.chr17[[1]]
> #exp.image[exp.image>3]=3
> #exp.image[exp.image< -3]=3
> #plotHeatmap(fit, datasets=list(cn.image,exp.image), type=c("gaussian","gaussian"),
> #  row.order=c(FALSE,FALSE), width=5, col.scheme=col.scheme)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>