Last year, when I joined my current company, I was tasked with preparing a teaching analysis environment for bioinformatics training. This opportunity introduced me to Pixi. Later, as my responsibilities shifted, I not only continued with bioinformatics analysis but also took on full‑stack maintenance work. The projects I handled spanned front‑end and back‑end, involving languages and frameworks beyond the data‑science staples R and Python. Pixi has still been able to serve as a cross‑language/stack development environment management tool (Conda offers an extremely rich resource pool, covering mainstream programming languages and common frameworks). Therefore, I’d like to summarize the practical Pixi features that have proven useful in my daily work.

In bioinformatics, the more cutting-edge your research direction, the more problems you’ll face from the informatics side. Even when papers are published with excellent results, and the original authors share their code or even provide ready-to-use software tools, it doesn’t mean we can easily use these existing resources for reproduction or further research. Chaotic environment setup is just one aspect - more often than not, since software authors aren’t professional software engineers, we should be grateful if the tool just works. We can’t expect these software to be bug-free, nor can we expect them to have decent performance (unless performance was a development goal). Even tools from well-established labs aren’t free to these issues, such as… Azimuth.

Milo is a differential abundance analysis method for single-cell RNA sequencing data that can detect compositional changes in cell neighborhoods across different conditions.