Add pyUCell

[mass-a]

Name of the tool: pyUCell

Short description: pyUCell is a computational method for robust and scalable single-cell signature scoring. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation is very fast and memory-efficient.

How does the package use scverse data structures (please describe in a few sentences): pyUCell relies on AnnData data structures and depends on scanpy. It can be integrated in existing scanpy pipelines and offers an alternative to scanpy.tl.score_genes()

• The code is publicly available under an OSI-approved license
• The package provides versioned releases
• The package can be installed from a standard registry (e.g. PyPI, conda-forge, bioconda)
• Automated tests cover essential functions of the package and a reasonable range of inputs and conditions [^1]
• Continuous integration (CI) automatically executes these tests on each push or pull request [^2]
• The package provides API documentation via a website or README[^3]
• The package uses scverse datastructures where appropriate (i.e. AnnData, MuData or SpatialData and their modality-specific extensions)
• I am an author or maintainer of the tool and agree on listing the package on the scverse website

Recommended

• Please announce this package on scverse communication channels (zulip, discourse, twitter)

• Please tag the author(s) these announcements. Handles (e.g. @scverse_team) to include are:

  • Zulip:
  • Discourse:
  • Mastodon:
  • Bluesky: @carmonation.bsky.social
  • Twitter: @carmonation

• The package provides tutorials (or "vignettes") that help getting users started quickly

• The package uses the scverse cookiecutter template.

[mikkelnrasmussen]

Hi @mass-a,

Thanks for submitting pyUCell - it looks like a very nice tool! I've had a look at the package and everything looks good to me.

Best,
Mikkel