| iCluster2 {iCluster} | R Documentation |
iCluster function with variance-weighted shrinkage (see Shen et al. PLoS ONE, 2012)
iCluster2(datasets, k, lambda=NULL, scale=T, scalar=F, max.iter=10, verbose=T)
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. |
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 |
Ronglai Shen shenr@mskcc.org
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
tune.iCluster2, plotiCluster, compute.pod, plotHeatmap
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)