plotHeatmap {iClusterPlus} | R Documentation

## 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,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)

### Arguments

`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

### Details

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.

### Value

no value returned.

### 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.

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

### See Also

`iCluster`,`iCluster2`

### Examples

    # see iManual.pdf

[Package iClusterPlus version 1.24.0 Index]