Add pylimma to ecosystem packages

[john-mulvey]

Summary

This PR proposes adding pylimma to the scverse ecosystem packages list.

Proposed metadata file:

• packages/pylimma/meta.yaml

Checklist for adding packages

Mandatory

Name of the tool: pylimma

Short description:
pylimma is a Anndata-native Python port of R limma, validated against the existing R package on real biological datasets. It has particular application to mass-spectrometry proteomics differential abundance analysis (as demonstrated in one of the package tutorials), alongside its traditional microarray, bulk RNA-seq, and pseudobulk single-cell use cases.

How does the package use scverse data structures (please describe in a few sentences):
Any function that consumes expression data (voom, lm_fit, normalize_between_arrays, array_weights, duplicate_correlation, etc.) accepts an AnnData object in place of a flat matrix. Outputs are written to adata.layers[...], fits are stored in adata.uns[...], and results are retrieved from AnnData via the usual top_table / decide_tests calls. This means that a pylimma workflow slots directly into an existing AnnData pipeline without any conversion step.

• 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
• Continuous integration (CI) automatically executes these tests on each push or pull request
• The package provides API documentation via a website or README
• 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

Beyond unit tests, correctness against the R reference is validated across ~230 function-level comparisons and using real biological datasets spanning microarray, RNAseq, proteomics, single-cell, and splicing workflows, with worst-case relative differences documented per function. Full methodology and results of the validation are available in the preprint

Recommended

• Please announce this package on scverse communication channels (zulip, discourse, twitter)
• Please tag the author(s) these announcements. Handles to include are:
  • Zulip:
  • Discourse:
  • Mastodon:
  • Bluesky:
  • Twitter:
• The package provides tutorials (or "vignettes") that help getting users started quickly
• The package uses the scverse cookiecutter template.