Hello Bioinformatics lovers,

Tommy here. Today, we will talk about data visualization, a critical skill for bioinformatics.

Type kuva box samples.tsv --group-col group --value-col expression --terminal and a box plot appears inside your shell. No file, no image viewer, the plot renders in the terminal itself.

kuva is a new scientific plotting library written in Rust. It covers 60 plot types, outputs SVG by default, and adds PNG and PDF through optional backends. The terminal rendering is the part worth trying first.

The visualization landscape now spreads across every language I use. I have relied on ggplot2 in R for years because the output is clean, and ggpubr layers the publication polish on top.

ComplexHeatmap has handled my heatmaps in Bioconductor for just as long.

Python has caught up: a reader emailed me recently about cnsplots, which builds Cell, Nature, and Science styled figures on matplotlib, and PyComplexHeatmap ports the same heatmap grammar I already know. Rust now has kuva.

The package matters less than the choice behind it. A volcano plot, a ridgeline, and a clustered heatmap all render cleanly whether or not they answer your question.

Pick the wrong chart and you bury the pattern you needed to show, and no library warns you. That judgment travels with you across every tool.

The tools, in one place:

• ggpubr: https://rpkgs.datanovia.com/ggpubr/​
• cnsplots: https://github.com/faridrashidi/cnsplots​
• PyComplexHeatmap: https://github.com/DingWB/PyComplexHeatmap​
• ComplexHeatmap: https://bioconductor.org/packages/ComplexHeatmap/​
• kuva: https://github.com/Psy-Fer/kuva​

Here are eight references I keep bookmarked for building that judgment:

1. Points of View, Nature Methods. The column series by Bang Wong and Martin Krzywinski. The color mapping and heatmap entries are excellent. http://blogs.nature.com/methagora/2013/07/data-visualization-points-of-view.html​
2. Ten Simple Rules to Colorize Biological Data Visualization, PLOS Computational Biology. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008259​
3. A curated dataviz resources collection. https://sabahzero.github.io/dataviz/resources​
4. Fundamentals of Data Visualization, Claus Wilke. https://clauswilke.com/dataviz/​
5. Data Visualization, Kieran Healy. https://socviz.co/​
6. R Graphics Cookbook, Winston Chang. http://www.cookbook-r.com/Graphs/​
7. ggplot2: Elegant Graphics for Data Analysis, Hadley Wickham. https://ggplot2-book.org/​
8. From Data to Viz, a decision tree that maps your data to the right chart. https://www.data-to-viz.com/​

I have read numbers 4 and 5 cover to cover and recommend both.

Which plotting library are you reaching for this year, and what pushed you to it? Reply and let me know.

Happy Learning!

Tommy aka crazyhottommy

PS:

If you want to learn Bioinformatics, there are four ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​
4. Lastly, I post daily on Linkedin

Stay awesome!