plotHMBayes: 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.

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)

`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

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.

no value returned.

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

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.

iClusterBayes,plotHeatmap