yeh234 / ComplexHeatmap

make complex heatmaps as well as self define annotation graphics

Make Complex Heatmaps

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential features. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics.

A single heatmap or a list of heatmaps are composed by basic components:

The package makes heatmaps in an object-oriented way by abstracting heatmaps into several classes:

• Heatmap a single heatmap
• HeatmapList a list of heatmaps
• HeatmapAnnotation annotation on columns or rows

and provides methods for arranging and plotting heatmap components.

Install

ComplexHeatmap is available on Bioconductor, you can intall it by:

source("http://bioconductor.org/biocLite.R")
biocLite("ComplexHeatmap")

If you want the latest version, install it directly from GitHub:

library(devtools)
install_github("jokergoo/ComplexHeatmap")

Usage

Make a single heatmap:

Heatmap(mat, ...)

A single Heatmap with column annotations:

ha = HeatmapAnnotation(df = anno1, anno_fun = anno2, ...)
Heatmap(mat, ..., top_annotation = ha)

Make a list of heatmaps:

Heatmap(mat1, ...) + Heatmap(mat2, ...)

Make a list of heatmaps and row annotations:

ha = HeatmapAnnotation(df = anno1, anno_fun = anno2, ..., which = "row")
Heatmap(mat1, ...) + Heatmap(mat2, ...) + ha

As a base package

ComplexHeatmap can be used as a base package to build other packages which focus On specific applications. E.g. EnrichedHeatmap package uses ComplexHeatmap as base to make heatmaps which visualize the enrichment of genomic signals to specific target regions.

Examples

The first examples is OncoPrint. The basic idea is to self define the heatmap body. Besides the default style which is provided by cBioPortal, there are additional barplots at both sides of the heatmap which show numbers of different alterations for each sample and for each gene.

The second examples visualizes correlation between methylation and expression, as well as other annotation information (data are randomly generated). In the heatmap, each row corresponds to a differentially methylated regions (DMRs). From left to right, heatmaps are:

1. methylation for each DMRs in samples.
2. direction of the methylation (one column heatmap), i.e. is methylation hyper in tumor or hypo?
3. expression for the genes that are associated with corresponding DMRs (e.g. closest gene).
4. signiciance for the correlation between methylation and expression (-log10(p-value)).
5. type of genes, i.e. is the gene a protein coding gene or a lincRNA?
6. annotation to gene models, i.e. is the DMR located in the intragenic region of the corresponding gene or the DMR is intergenic?
7. distance from the DMR to the TSS of the corresponding gene.
8. overlapping between DMRs and enhancers (Color shows how much the DMR is covered by the enhancers).