plotHeatmap: A function to generate heatmap panels sorted by integrated...
In iClusterPlus: Integrative clustering of multi-type genomic data

Description Usage Arguments Details Value Author(s) References See Also Examples

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

plotHeatmap(fit,datasets,type=c("gaussian","binomial","poisson","multinomial"),
	sample.order=NULL,row.order=NULL,sparse=NULL,threshold=rep(0.25,length(datasets)),
	width=5,scale=rep("none",length(datasets)),col.scheme=rep(list(bluered(256)),
	length(datasets)), chr=NULL, plot.chr=NULL, cap=NULL)

`fit`	A iCluster object.
`datasets`	A list object of data matrices.
`type`	Types of data in the datasets.
`sample.order`	User supplied cluster assignment.
`row.order`	A vector of logical values each specificy whether the genomic features in the corresponding data matrix should be reordered by similarity. Default is TRUE.
`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.
`width`	Width of the figure in inches
`scale`	A vector of logical values each specify whether data should be scaled. Default is FALSE.
`col.scheme`	Color scheme. Can use bluered(n) in gplots R package.
`chr`	A vector of chromosome number.
`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.
`cap`	Image color option

The samples are ordered by the cluster assignment using the R code: order(fit$clusters). For each data set, the features are ordered by hierarchical clustering of the features using the complete method and 1-correlation coeffient as the distance.

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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

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