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.

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.

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.

If you are considering creating a Python package I strongly suggest cookiecutter-scverse by @scverse.bsky.social

It’s a template that already incorporates many useful features.

github.com/scverse/cook...

We identified a cellular niche of hypoxic tumor cells always in contact with a niche enriched in neutrophils. The same samples contained proliferative tumor cells that didn't show the same pattern.

This result was observed in three independent spatial omics datasets.

Once cellular niches are identified, CellCharter implements a suite of methods to characterize them and compare them between conditions (e.g. healthy vs disease).

For example, we can compare niches based on their shape in the tissue.

Cell niches are communities of cells of different types that interact to carry out a function, and for that, these cells need to be close to each other.

Spatial -omics technologies can be used to find cell niches because they measure both the phenotype and location of cells.