Great work @grst.bsky.social ! I remember there were some differences between DESeq2 (R) and pyDESeq2 (Python).

Do you think it's safe to assume that pyDESeq2 is also the best approach for the simple case? :)

And for anyone who has considered contributing to an open source package: don't be scared to propose changes.

Even if it's not a complete and perfect solution, whoever is maintaining the package will help you in get to the right solution and they will be incredibly grateful.

For people like me who don't have a team behind a package like CellCharter, contributions like these mean a lot. So thank you Lukas :)

And congratulations, it's not always easy to jump into an existing codebase and propose changes.

RCS measures how large a cell niche (aka spatial domain aka spatial cluster) is compared to what would be normally expected.

This pushed me to completely rewrite the system for generating and plotting boundaries for cell niches.
The new system is now more efficient, consistent, and visually clear.

By the way, here in Switzerland the public defense is meant for the general public, so the presentation is understandable by anyone, and I even sneaked some jokes inside.

The only sort of gatekeeper I wish we saw more of were those who wanted better software. Who cares if your model performs the best if it's unusable, or if your pipeline aligs reads faster if it only works on your local work computer? Or if there's no documentation on how to use any of it.

3. How spatial omics can identify cell communities and true cell interactions to understand what is really happening.

4. How discoveries driven by spatial omics will lead to new effective therapies.

1. How two types of cells, fibroblasts and macrophages, can cooperate to support cancer.

2. Why some of the therapies targeting only one of those cell types failed in clinical trials.

Bonus tip: if you are considering creating a package to be included within the scverse ecosystem, the cookiecutter template already sets up 5 of the 8 mandatory requirements.

A common problem with templates and frameworks is that they tend to restrict what you can do to keep you consistent.

But this is not the case here.

I used the scverse template even for projects that are not strictly related to single-cell or computational biology.

It sets up beautiful documentation generated directly from your code like in the picture.

Finally, it gives automatic actions for publishing and updating the package.

Every time you push code to your main GitHub branch, scverse’s cookiecutter automatically runs tests.

Even better, it supports testing with pre-release dependencies!

This means you can detect breaking changes in upcoming dependency updates before they're officially released.

Same as what was said about React, Vue may be a bit overkill unless you want 100% customization.

But if you are interested or you want to use it as an excuse to learn Vue.js, there is also an official project called VuePress that is focused on showing content using Markdown

You mean installing VS Code? It should be much easier than something like the Linux subsystem.

You just need to download the VS Code file and click install