Fig 1.
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.
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.
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.
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.