plotHeatmap: A function to generate heatmap panels sorted by integrated cluster assignment.

Description

A function to generate heatmap panels sorted by integrated cluster assignment.

Usage

plotHeatmap(fit, datasets, sample.order=NULL, feature.order=NULL, 
width=5, scale=NULL, col.scheme=NULL, sparse=NULL, threshold=NULL, 
chr=NULL, plot.chr=NULL, cap=NULL)

Arguments

fit

A iCluster object

datasets

A list object of data matrices

feature.order

A vector of logical values each specificy whether the genomic features in the corresponding data matrix should be reordered by similarity. Default is FALSE.

sparse

A vector of logical values each specificy whether to plot the top cluster-discriminant features. Default is FALSE.

threshold

When sparse is TRUE, a vector of threshold values to include the genomic features for which the absolute value of the associated coefficient estimates fall in the top quantile. threshold=c(0.25,0.25) takes the top quartile most discriminant features in data type 1 and data type 2 for plot.

plot.chr

A vector of logical values each specificy whether to annotate chromosome number on the left of the panel. Typically used for copy number data type. Default is FALSE.

chr

A vector of chromosome number.

col.scheme

Color scheme. Can use bluered(n) in gplots R package.

sample.order

User supplied cluster assignment.

width

Width of the figure in inches

cap

Image color option

scale

A vector of logical values each specify whether data should be scaled. Default is FALSE.

Value

no value returned.

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

Examples

Run this code

#library(iCluster)
#data(gbm)
#data(coord)
#chr=chr[,1]
#fit=iCluster2(datasets=gbm, k=3, lambda=list(0.44,0.33,0.28))           
#plotHeatmap(fit=fit, datasets=datasets, feature.order=c(FALSE,TRUE,TRUE),
#sparse=c(FALSE,TRUE,TRUE),plot.chr=c(TRUE,FALSE,FALSE), chr=chr)