Prior publications in the biological heatmap visualization field have focused predominantly on the production of static heatmaps, which do not constitute a computational challenge and are relatively simple to make using limited computational resources. In contrast, interactive heatmaps present a unique software engineering challenge due to the extensive memory requirements needed to operate efficiently on big datasets (heatmap rendering speed, zooming speed, hover speed, etc.).
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.
Researchers from the University of Miami Miller School of Medicine have developed 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 10^5 – 10^7 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed.
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.
The developers tested shinyheatmap on gene expression datasets ranging from 10,000 rows to 300,000 rows and achieved speeds >100,000 faster than previous state-of-the-art interactive heatmap software. They performed these benchmarks on a standard Windows desktop machine (64-bit Windows 10 Pro desktop machine with 16.0 GB of RAM and an Intel(R) Core(TM) i7-5820K CPU at 3.30 GHz), a common workstation employed in academic biology labs, implying that even better performance is achievable on more powerful workstations.
Availability –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.