| plotHeatmap {iCluster} | R Documentation |
A function to generate heatmap panels sorted by integrated cluster assignment.
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)
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. |
no value returned.
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
#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)