Table of contents:
A 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. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms. Heatmaps are used in many fields for visualizing observations, correlations, missing values patterns, and more.
Interactive heatmaps allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the heatmap by dragging a rectangle around the relevant area.
This work is based on the ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap (or the static heatmap.2 from gplots), with the advantage of more features such as speed (plotly.js is able to handle larger size matrix), sidebar annotation, and the ability to zoom from the dendrogram.
This package is still under active development. If you have features you would like to have added, please submit your suggestions (and bug-reports) at: https://github.com/talgalili/heatmaply/issues
You can see the most recent changes to the package in the NEWS.md file
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
To install the stable version on CRAN:
install.packages('heatmaply')To install the latest (“cutting-edge”) GitHub version run:
# good packages to install for this to work smoothly:
install.packages(c("Rcpp","ggplot2","munsell","htmltools","DBI","assertthat",
"gridExtra","digest","fpc","TSP","registry","gclus","gplots","RColorBrewer",
"stringr","labeling","yaml"))
# You'll need devtools
install.packages.2 <- function (pkg) if (!require(pkg)) install.packages(pkg);
install.packages.2('devtools')
# make sure you have Rtools installed first! if not, then run:
#install.packages('installr'); install.Rtools()
devtools::install_github("ropensci/plotly") # you will probably benefit from the latest version of plotly
devtools::install_github('talgalili/heatmaply')And then you may load the package using:
library("heatmaply")Quick example:
library(heatmaply)
heatmaply(mtcars, k_row = 3, k_col = 2)For more (interactive) examples see the online vignette on CRAN
There are also more complex biological examples of using heatmaply in the R package heatmaplyExamples (hosted on github). Here are direct links for available examples:
You can save an interactive version of your heatmaply into an HTML file using the following code:
dir.create("folder")
library(heatmaply)
heatmaply(mtcars, file = "folder/heatmaply_plot.html")
browseURL("folder/heatmaply_plot.html")Similar code can be used for saving a static file (png/jpeg/pdf)
dir.create("folder")
library(heatmaply)
# Before the first time using this code you may need to first run:
# webshot::install_phantomjs()
heatmaply(mtcars, file = "folder/heatmaply_plot.png")
browseURL("folder/heatmaply_plot.png")The package d3heatmap (https://github.com/talgalili/d3heatmap) is no not actively maintained.
If users are interested in replacing their d3heatmap
functions, this is easily done with heatmaply:
d3heatmap::d3heatmap() can be replaced
with heatmaply::heatmaply()d3heatmap for shiny applications
can use the following functions:
d3heatmap::d3heatmapOutput() can be replaced with
plotly::plotlyOutput()d3heatmap::renderD3heatmap() can be replaced with
plotly::renderPlotly()This package is thanks to the amazing work done by MANY people in the open source community. Beyond the many people working on the pipeline of R, thanks should go to the people working on ggplot2 (Hadley Wickham, etc.) and plotly (Carson Sievert, etc.). Also, many of the design elements were inspired by the work done on heatmap, heatmap.2 and d3heatmap, so special thanks goes to the R core team, Gregory R. Warnes, and Joe Cheng from RStudio. The dendrogram side of the package is based on the work in dendextend, in which special thanks should go to Andrie de Vries for his original work on bringing dendrograms to ggplot2 (which evolved into the richer ggdend objects, as implemented in dendextend).
The work on heatmaply was done by Tal Galili, Alan O’Callaghan, and Jonathan Sidi (mostly on shinyHeatmaply).
Funding: This work was supported in part by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).
The methods within the package can be cited as:
Tal Galili, Alan O'Callaghan, Jonathan Sidi, Carson Sievert; heatmaply: an R package for creating
interactive cluster heatmaps for online publishing, Bioinformatics, , btx657,
https://doi.org/10.1093/bioinformatics/btx657A BibTeX entry for LaTeX users is
@Article{,
author = {{Galili} and {Tal} and {O'Callaghan} and {Alan} and {Sidi} and {Jonathan} and {Sievert} and {Carson}},
title = {heatmaply: an R package for creating interactive cluster heatmaps for online publishing},
journal = {Bioinformatics},
year = {2017},
doi = {10.1093/bioinformatics/btx657},
url = {https://dx.doi.org/10.1093/bioinformatics/btx657},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btx657/21358327/btx657.pdf},
}This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles.
You are welcome to: