plotHMBayes {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

    plotHMBayes(fit, datasets, type = c("gaussian", "binomial", "poisson"),
        sample.order = NULL, row.order = NULL, sparse = NULL,
        threshold = rep(0.5,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 iClusterBayes 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 specify whether the genomic features in the corresponding data matrix should be reordered by similarity. Default is TRUE.
`sparse` | A vector of logical values each specify 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 on the heatmap. Each data set should have a threshold. For each data set, a feature with posterior probability greater than the threshold will be included. Default value is 0.5 for each data set.
`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 specify 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 by 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 coefficient as the distance.

### Value

no value returned.

### Author(s)

Ronglai Shen shenr@mskcc.org,Qianxing Mo qianxing.mo@moffitt.org

### References

Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG. (2018). A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19(1):71-86.

### See Also

`iClusterBayes`,`plotHeatmap`

### Examples

    # see iManual.pdf

[Package iClusterPlus version 1.18.0 Index]