{"categories":[{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"TileDB-SOMA is a Python implementation of the SOMA API specification for storing and retrieving single-cell genomic data using TileDB as the underlying storage engine.","skillfed_tags":["single-cell-genomics","data-storage","bioinformatics"],"use_cases":["Store and query large single-cell RNA-seq datasets in a standardized, interoperable format using TileDB's efficient columnar storage.","Integrate single-cell data workflows with tools like scanpy and anndata while maintaining SOMA compliance across different analysis pipelines.","Configure TileDB-specific storage options (filters, dimensions, compression) for optimized performance on custom single-cell datasets.","Access single-cell data through a standardized API that abstracts away storage backend details, enabling portability between implementations.","Build reproducible bioinformatics pipelines that rely on a unified data model for single-cell genomics across multiple research groups."],"what_it_does":"TileDB-SOMA provides a Python API for the Unified Single-cell Data Model (SOMA), an open standard for organizing and accessing single-cell genomic data. It uses TileDB, a columnar array storage engine, as its backend to enable efficient storage and retrieval of large single-cell datasets. The package implements the full SOMA specification, allowing researchers and bioinformaticians to work with standardized single-cell data formats across different tools and platforms.\n\nThe package integrates with the popular single-cell Python ecosystem\u2014it depends on anndata, scanpy, pandas, numpy, and pyarrow\u2014making it a bridge between TileDB's storage capabilities and existing bioinformatics workflows. It supports platform-specific configuration through a TypeScript-style interface, allowing fine-grained control over TileDB storage parameters like filters, cell order, and capacity settings.","worth_installing":"Yes, if you work with single-cell genomic data and need standardized, efficient storage. The package is actively maintained, supports modern Python versions (3.9\u20133.13), has no known vulnerabilities, and integrates well with the established scanpy and anndata ecosystem. Medium install friction is manageable for most development environments. Not necessary if you are already satisfied with your current single-cell data storage and don't require SOMA compliance or TileDB's specific performance characteristics."},"id":"tiledbsoma","links":{"html":"https://skillfed.io/packages/tiledbsoma","md":"https://skillfed.io/packages/tiledbsoma.md","pypi":"https://pypi.org/project/tiledbsoma/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-27","license_spdx":null,"license_treatment":"permissive","name":"tiledbsoma","python_support":"supports_current","summary":"Python API for efficient storage and retrieval of single-cell data using TileDB"},"popularity":{"monthly_downloads":100806,"position":12973,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.0"}