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. 2017 May 11;12(5):e0176334.
doi: 10.1371/journal.pone.0176334. eCollection 2017.

shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics

Bohdan B Khomtchouk  1   2 , James R Hennessy  3 , Claes Wahlestedt  1   2
Affiliations

shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics

Bohdan B Khomtchouk et al. PLoS One. .

Abstract

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps.

Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 105-107 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed.

Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. shinyheatmap static heatmap.
shinyheatmap UI showcasing the visualization of a static heatmap generated from a large input dataset. Parameters such as hierarchical clustering (including options for distance metrics and linkage algorithms), color schemes, scaling, color keys, trace, and font size can all be set by the user. Progress bars appear during the heatmap rendering process to alert the user if any technical issues may arise. Sample input files of various sizes are provided as part of the web application, whose source code can be viewed on Github.
Fig 2
Fig 2. shinyheatmap interactive heatmap.
shinyheatmap UI showcasing the visualization of an interactive heatmap generated from a large input dataset. An embedded panel that appears top right on-hover provides extensive download, zoom, pan, lasso and box select, autoscale, reset, and other features for interacting with the heatmap.
Fig 3
Fig 3. fastheatmap & shinyheatmap are linked together.
A) shinyheatmap contains an auto-detector that detects the size of a user’s input matrix and, if the input matrix is too large, the user will be provided with a direct link to access shinyheatmap’s high performance computing server: fastheatmap. B) fastheatmap UI upon clicking on the URL link shown in Panel A.
Fig 4
Fig 4. shinyheatmap performance benchmarks.
shinyheatmap’s HPC plug-in, fastheatmap, performs >100000 faster than other state-of-the-art interactive heatmap software. “Number of Rows” denotes the number of rows in the input file, “inf” (infinity) denotes a system crash due to memory overload, “s” denotes seconds, “min” denotes minutes, and “ms” denotes milliseconds.
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