To be honest, I was a bit sceptical when I first tried plot.ly a couple of years ago. The functionality was limited, company focus seemed to be on enterprise data science, besides, the inability to keep my plots private was the last turn off. It is, sadly, still the reality of academia: you need to keep quiet until you publish.
Plot.ly have come a long way since then though!
- academics can get a private account, just contact plot.ly describing your project
- If you’ve created graphs in Matlab, R, Julie, or Python, then you can import them into plotly and share online, so your colleagues can further modify and play with the data. Yes, please!
- Plot.ly central mission now is supporting open science, according to their co-founder Matt Sundquist
- Effortless integrations with the tools you are already using thanks to their APIs
For all these reasons, I’ve decided it’s worth the time to really tell people about it and how to use it to plot the typical figures that computational biologists usually need. Have a look at this IPython notebook I put together showcasing how to make interactive heatmaps of gene expression, scatter plots and histograms as well as interaction network visualizations using plot.ly.
You can contribute to the showcase of beautiful plots too, just check out their github repo for IPython notebooks.