Source-linked AI summary
heatmaply: an R package for creating interactive cluster heatmaps for online publishing
Tal Galili, Alan OCallaghan, Jonathan Sidi, Carson Sievert
TL;DR
Academic publishing often reduces interactive cluster heatmaps to static figures or requires provider-specific tools or JavaScript expertise. heatmaply creates self-contained, shareable HTML heatmaps with hovering and zooming, supporting interactive publication and use in R-based workflows.
Problem
Published cluster heatmaps are often static figures, while existing interactive options may depend on online providers or JavaScript knowledge.
Method
The heatmaply R package generates self-contained HTML cluster heatmaps with client-side interactivity from users’ data.
Results
The resulting figures support cell-value tooltips, zooming, and sharing through journal supplements, websites, RStudio, Shiny, and HTML documents.
Takeaways & Limitations
heatmaply enables publication-ready interactive cluster heatmaps without requiring readers to use the original analysis environment.
Abstract
from arXiv · showhide
Summary: heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. Availability and implementation: The heatmaply package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=heatmaply. Supplementary information: Supplementary data are available at Bioinformatics online.
1 Introduction
heatmaply addresses the gap between static published heatmaps and existing interactive tools by generating shareable, self-contained HTML cluster heatmaps from R. The resulting figures support zooming and hover-based value inspection across several publication and application contexts.
- Cluster heatmaps visualize high-dimensional data as ordered, colored cell matrices often accompanied by dendrograms and categorical annotations.
- Existing interactive options may depend on specific online providers or require JavaScript knowledge, while academic publications commonly provide only static PNG or PDF figures.
- heatmaply generates client-side JavaScript and a shareable HTML file containing an interactive cluster heatmap from the user’s data.
- The self-contained figure supports zooming and hover-based value inspection and can be shared online, viewed in RStudio, included in Shiny, or embedded in knitr/RMarkdown HTML documents.
- The paper provides guidelines for effective cluster heatmap visualization, with examples based on Project Tycho and real-world biological data.
2 heatmaply: a simple example
The example shows that creating a useful cluster heatmap requires deliberate choices about data transformation, color palettes, and dendrogram construction. heatmaply supports these choices interactively, while shinyHeatmaply provides a GUI and exportable, reproducible HTML workflows.
- 2 heatmaply: a simple example: Heatmap construction requires decisions about transforming the data matrix, selecting a color palette, and configuring the dendrogram.Raw data may need transformation to provide a meaningful, comparable scale, while clustering requires choices about distance and linkage.
- 2 heatmaply: a simple example: An ideal heatmap palette is colorful, perceptually uniform, and robust to colorblindness.These properties aim to make differences visible while keeping visual differences consistent with numerical differences.
- 2 heatmaply: a simple example: The example preserves year order by removing the column dendrogram and computes the row dendrogram with Euclidean distance and average linkage.Users can instead select alternative clustering functions, distance measures, linkage functions, or dendrogram placement.
- 2 heatmaply: a simple example: shinyHeatmaply offers a GUI to guide heatmap construction and can export HTML heatmaps with parameter summaries for reproduction.The heatmaplyExamples package provides a more detailed step-by-step demonstration on biological datasets.