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Thursday, July 6 • 5:50pm - 5:55pm
**heatmaply**: an *R* package for creating interactive cluster heatmaps

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Keywords: cluster heatmap, interactive visualization, ggplot2, plotly, shiny
Webpages: heatmaply, shinyHeatmaply
A cluster heatmap is a popular graphical method for visualizing high dimensional data, in which a table of numbers are encoded as a grid of colored cells (Wilkinson and Friendly 2009, Weinstein (2008)). The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms and extra columns of categorical annotation. Heatmaps are used in many fields for visualizing observations, correlations, and missing values patterns. There are many R packages and functions for creating static heatmap figures (the most famous one is probably gplots::heatmap.2).
The heatmaply R package allows the creation of interactive cluster heatmaps, enabling tooltip hover text and zoom-in capabilities (from either the grid or the dendrograms), while supporting sidebar annotation. The package brings together many well known packages such as ggplot2 (Wickham 2016), plotly, viridis, seriation (Hahsler, Hornik, and Buchta 2008), dendextend (Galili 2015), and others. Also, it is now supported by the shinyHeatmaply shiny app.
You can play with a simple interactive example by running:
install.packages('heatmaply'); library('heatmaply') heatmaply(percentize(mtcars), k_row = 4, k_col = 2, margins = c(40,120,40,20)) This talk will provide an overview of design principles for creating a useful, and beautiful, cluster heatmap. Attention will be given to data preprocessing, choosing a color palette, and careful dendrograms creation.
This work was made possible thanks to the essential contribution of Jonathan Sidi, Alan O’Callaghan, Carson Sievert, and Yoav Benjamini. As well as the joint work of Joe Cheng and myself on the d3heatmap package (which laid the foundation for heatmaply). The speaker is the creator of the R packages installr, dendextend, and heatmaply, and blogs at: www.r-statistics.com.
References Galili, Tal. 2015. “Dendextend: An R Package for Visualizing, Adjusting and Comparing Trees of Hierarchical Clustering.” Bioinformatics. Oxford Univ Press, btv428.

Hahsler, Michael, Kurt Hornik, and Christian Buchta. 2008. “Getting Things in Order: An Introduction to the R Package Seriation.” Journal of Statistical Software 25 (3). American Statistical Association: 1–34.

Weinstein, John N. 2008. “A Postgenomic Visual Icon.” Science 319 (5871). American Association for the Advancement of Science: 1772–3.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer.

Wilkinson, Leland, and Michael Friendly. 2009. “The History of the Cluster Heat Map.” The American Statistician 63 (2). Taylor & Francis: 179–84.


Thursday July 6, 2017 5:50pm - 5:55pm CEST
2.02 Wild Gallery