Dash and the R programming language offer innovative avenues for bioinformaticians, pharmaceutical developers, and researchers to synergistically analyze, visualize, and present data within an interactive web application.
In addition to the Alignment and Sequence Viewer components mentioned above, a range of other Dash Bio components are available to complement a variety of analysis objectives. Below are a few of my personal favorite figures which can be produced using the package:
- Circos plots
- Ideogram plots
- Manhattan plots
- Needle plots
- Volcano plots
- Two-dimensional molecular visualizations
- Three-dimensional molecular visualizations (rendered with 3Dmol.js or Speck)
- Genomic alteration visualizations using OncoPrint
So, what do these components look like? Let’s look at a few in detail.
The Needle Plot component makes it easy to produce responsive, dynamic visualizations of large datasets. The lines and markers in the figure are analogous to bars in a histogram:
Manhattan and Volcano Plots
Users of Sahir Bhatnagar’s excellent manhattanly package in R will be pleased to see full support for embedding Manhattan and Volcano plots in Dash for R apps.
The Dash Bio package also provides support for the Broad Institute’s Ideogram library, making it straightforward to display chromosomes and specific gene locations as bands, in multiple views.
The component can display homology between chromosomes, a brush overview, and also offers gene annotations to highlight points of interest. NCBI taxonomy IDs may also be used to refer to chromosomes from different species.
The OncoPrint component simplifies visualizations of genomic events including mutations, amplifications, and regulation, while making it easy to subset data and color code features of interest.
Want to see more?
This overview is just a glimpse of what’s possible with Dash Bio and R; for a closer look, please check out our comprehensive, interactive documentation. We regularly add new functionality via additional components, so check back often or drop us a comment and let us know what else you’d like to see.
Installing Dash Bio from within R is easy; if you don’t already have Dash for R, install that first using install_github:
install_github(“plotly/dashR”, upgrade = TRUE)
Once Dash for R is installed, downloading the dashBio R package is straightforward:
install_github(“plotly/dash-bio”, upgrade = TRUE)
One more thing…
If you like where we’re going with Dash for R, head over to these freshly minted GitHub projects and give them a star:
If you’re a lab, chemical company, or drug development company, and you would like a customized Dash app or component built for you, please get in touch—we love a challenge. We also offer workshops in case you’d like to take a deeper dive into Dash for R.