Figure 7:

Interactive visualizations facilitate sharing and repeatability. A. Interactive visualization dashboard in the Pavian Shiny app for metagenomic analysis [64, 65]. Shiny allows you to build interactive web pages using R code. Data are manipulated by R code in real time in a web page, producing analysis and visualizations of a dataset. Shiny apps can contain user-specifiable parameters, allowing a user to control visualizations or analyses. In (A), sample PT1 is selected, and taxonomic ranks class and order are excluded. Shiny apps allow collaborators who may or may not know R to modify R visualizations to fit their interests. B. Plotly heat map of transcriptional profiling in human brain samples [66]. Hovering over a cell in the heat map displays the sample names from the x and y axis, as well as the intensity value. Plotting tools such as plotly and vega-lite produce single interactive plots that can be shared with collaborators or integrated into websites [67, 68]. Interactive visualizations are also helpful in exploratory data analysis.