Introduction to ComplexHeatmap package

Author: Zuguang Gu ( [email protected] )

Date: 2018-06-19

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

General design

Generally, a heatmap list contains several heatmaps and row annotations.

Surrounding the heatmap list, there are legends for heatmaps and annotations, also there are titles which are placed on the four sides of the heatmap list. And for each heatmap, there are also different components surrounding the heatmap body.

The ComplexHeatmap package is implemented in an object-oriented way. To describe a heatmap list, there are following classes:

• Heatmap class: a single heatmap containing heatmap body, row/column names, titles, dendrograms and column annotations.
• HeatmapList class: a list of heatmaps and row annotations.
• HeatmapAnnotation class: defines a list of row annotations and column annotations.

There are also several internal classes:

• SingleAnnotation class: defines a single row annotation or column annotation.
• ColorMapping class: mapping from values to colors.

ComplexHeatmap is implemented under grid system, so users should know basic grid functionality to get full use of the package.

Vignettes

There are several vignettes in the package. Each vignette focuses on one specific topic. Following lists the general topics discussed in these vignettes:

1. Making a Single Heatmap

   This vignette introduces the basic configuration for making a single heatmap. Similar as other R functions/packages, the basic usage is quite similar, but there are several unique features for ComplexHeamtap package.

   • Works both for numeric matrix and character matrix.
   • For numeric matrix which contains continuous values, the package allows a color mapping function which can give more accurate colors and be robust to outliers.

   • Highly flexible for clustering. You can define the distance method for clustering by:

     • a pre-defined distance such as “euclidean” or “pearson”,
     • a self-defined function which calculates distance from a matrix,
     • a self-defined function which calculates distance from two vectors.

     You can define the clustering method by:

     • a clustering function such as diana() from cluster package
     • a hclust or dendrogram object.

   • NA is allowed for clustering and heatmap visualization.

   • Dendrogram and dimension names can be put on any side of the heatmap.

   • Rows on the heatmap can be split by cutree, by kmeans or by a data frame which contains different levels that split the heatmap.

   • The heatmap body itself can be completely self-defined.

2. Making a List of Heatmaps

   This vignette introduces how to concatenate a list of heatmaps and how adjustment is applied to keep the correspondence of the heatmaps.

3. Heatmap Annotations

   This vignette introduces the concept of the heatmap annotation and demonstrates how to make simple annotations as well as complex annotations. Also, the vignette explains the difference between column annotations and row annotations.

4. Heatmap and Annotation Legends

   This vignette introduces how to configurate the heatmap legend and annotation legend, also how to add self-defined legends.

5. Heatmap Decoration

   This vignette introduces methods to add more self-defined graphics to the heatmaps after the heatmaps are generated.

6. Interactive with Heatmaps

   How to select a region in the heatmap to retrieve the sub-matrix.

7. OncoPrint

   How to make an oncoPrint.

8. Examples

   More simulated and real-world examples are shown in this vignette.

In the supplementaries of the ComplexHeatmap paper, there are four comprehensive examples which are applied on real-world high-throughput datasets. The examples can be found here.

Also my blog has some examples and tips for making better complex heatmaps.