somacore
Python-language API specification and base utilities for implementation of the SOMA system.
Decision gist · record as of 2026-08-14
Yes, if you are implementing a SOMA backend or building libraries that work with SOMA data. No, if you are an end user working with single-cell data—install a SOMA implementation like tiledbsoma instead. The package is actively maintained, has no known vulnerabilities, and carries a permissive license, but is explicitly not intended for direct end-user use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low install friction with a pure-Python wheel and nine well-established dependencies (numpy, pandas, scipy, anndata, pyarrow, and others).
- Repository is active with recent commits and no security vulnerabilities reported.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive) places no restrictions on use, modification, or distribution in commercial or private projects.
last release 2026-04-17 (119 days) · last repo commit 2026-07-24 · 85 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 89,712 downloads/mo, #13,639 on PyPI
Alternatives
Verify before relying
pip install somacore
import somacore
# Access SOMA base interfaces and types for implementing or working with SOMA data- Whether this package is intended for direct end-user installation or primarily as a transitive dependency of SOMA implementations like tiledbsoma.
- Specific use cases beyond implementing SOMA backends or building SOMA-aware libraries.
What it is and what it does
somacore is the Python reference implementation of the abstract SOMA specification, a standardized format for storing and retrieving single-cell data. It provides base interfaces, shared types, and cross-implementation utilities rather than a complete storage backend. The package is designed primarily for SOMA implementors and libraries that handle SOMA data, not for end users working directly with single-cell datasets.
The package depends on established scientific Python libraries (numpy, pandas, scipy, anndata, pyarrow) and provides the foundational API contracts that concrete SOMA implementations like tiledbsoma build upon. If you are working with SOMA data, the documentation recommends installing a SOMA implementation instead of this core package directly.
Use it for
- Building a SOMA backend implementation that needs to conform to the standardized SOMA interface specification.
- Developing libraries that read or write SOMA-formatted single-cell data and need shared type definitions.
- Contributing to or extending the SOMA ecosystem with new storage or retrieval mechanisms.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are implementing a SOMA backend or building libraries that work with SOMA data.
No, if you are an end user working with single-cell data—install a SOMA implementation like tiledbsoma instead. The package is actively maintained, has no known vulnerabilities, and carries a permissive license, but is explicitly not intended for direct end-user use.
Install
somacore on PyPI
Before you install
Low install friction with a pure-Python wheel and nine well-established dependencies (numpy, pandas, scipy, anndata, pyarrow, and others). Repository is active with recent commits and no security vulnerabilities reported.
Requires Python 3.9 or later.
License in practice
MIT license (permissive) places no restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install somacore
import somacore
# Access SOMA base interfaces and types for implementing or working with SOMA data
Verify before relying
- Whether this package is intended for direct end-user installation or primarily as a transitive dependency of SOMA implementations like tiledbsoma.
- Specific use cases beyond implementing SOMA backends or building SOMA-aware libraries.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesanndataattrsnumpypandaspyarrowpyarrow-hotfixscipyshapelytyping-extensions |
| Maintenance | Actively maintained 119 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 89,712 / month, #13,639 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT License |
Evidence: somacore-2.0.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “SOMA specification Python”
- somacoreProvides the Python reference implementation of the SOMA…
- tiledbsomaTileDB-SOMA is a Python implementation of the SOMA API specification…
- pipfileProvides a design specification and parser for Pipfile and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also tiledbsoma · cellxgene-census · scvi-tools · scanpy · pystac-ext-raster · bionty · mudata · pystac · refgenie · sarif-om