add flowsom meta.yaml

[berombau]

Checklist for adding packages

Mandatory

Name of the tool: FlowSOM

Short description: The complete FlowSOM package known from R, now available in Python!
Analyze high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map (SOM). FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology.

How does the package use scverse data structures (please describe in a few sentences): MuData is used to store cell clustering and metaclustering.

• 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) (depends on PEP 541 Request: FlowSOM pypi/support#3247 and Add recipe for flowsom and dependencies conda-forge/staged-recipes#26252)
• The package uses automated software tests and runs them via continuous integration (CI)
• 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

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:
  • Twitter: @saeyslab
  • Zulip:
  • Discourse:
  • Mastodon:
• The package provides tutorials (or "vignettes") that help getting users started quickly
• The package uses the scverse cookiecutter template.

[grst]

Hi Benjamin,

LGTM!

You might be interested in the following discussions about harmonizing flow cytometry data analysis within scverse:
scverse/governance#64
scverse/pytometry#47

There has also been added an implementation of FlowSOM to pytometry: https://github.com/scverse/pytometry/pull/59/files
I understand your package adds some nice visualization on top of that, but do you have any idea who the implementations compare in terms of speed?