Abstract
Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.
Highlights
Interactive data visualization is increasingly imperative in the biological sciences [1]
We introduce technology that allows multiple independent layers of interactive visualization written in open-source code
The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools
Summary
Interactive data visualization is increasingly imperative in the biological sciences [1]. Interactive visualization tools for genomic data can have restricted access when only available on certain operating systems and/or when requiring payment [3,4,5]. These limitations can be removed when tools are published on open-source repositories. User uses Shiny buttons to specify treatment pairs and hexagon sizes for drawing background hexagons. Foreground User uses Shiny buttons to specify metric, metric order, and point size for drawing foreground points. Foreground User uses Shiny buttons to specify point size, log fold changes, p-values to draw foreground points. We achieved our independent double-layered interactivity using htmlwidgets [17], ggplot2 [18], shiny [19], JavaScript, and plotly [20]
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