Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics.
The InteractiveComplexHeatmap package can directly export static complex heatmaps into an interactive Shiny app. Have a try!
Zuguang Gu, et al., Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics, 2016.
Zuguang Gu. Complex Heatmap Visualization, iMeta, 2022.
ComplexHeatmap is available on Bioconductor, you can install it by:
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("ComplexHeatmap")
If you want the latest version, install it directly from GitHub:
Make a single heatmap:
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
The full documentations are available at https://jokergoo.github.io/ComplexHeatmap-reference/book/ and the website is at https://jokergoo.github.io/ComplexHeatmap.
There are following blog posts focusing on specific topics:
- Make 3D heatmap
- Translate from pheatmap to ComplexHeatmap
- Set cell width/height in the heatmap
- Interactive ComplexHeatmap
- Word cloud as heatmap annotation
- Which heatmap function is faster?
- Rasterization in ComplexHeatmap
- Block annotation over several slices
- Integrate ComplexHeatmap with cowplot package
Visualize Methylation Profile with Complex Annotations
Correlations between methylation, expression and other genomic features
Visualize Cell Heterogeneity from Single Cell RNASeq
Making Enhanced OncoPrint
MIT @ Zuguang Gu