Interactive visualisation is powerful and getting more popular. Although static images are still major parts in scientific publications, I believe that interactive graphs will become more and more, at least in the online version of publications.
(Title image from Computer World)
Although it’s normally editor’s responsibility, we do benifit a lot if we can use them.
For charts like bar plot, line chart, or even volcano plot, we can use the data directly in JS script. For PCA plot, we can export the pca matrix first, e.g. from DESeq2
pca_data <- plotPCA(data, intgroup=c("conditions"), returnData = TRUE)
or to export Heatmap matrix data, then make a chart.
- Plotly has it’s own R package to make interactive charts
- If you prefers to use ggplot2, there is also Plotly ggplot2 Library
- R package for Highcharts is also developed with easy usage
- recharts: an R interface to ECharts