BiocPy brings Bioconductor's core data structures and analysis tools to the Python ecosystem. These structures, including BiocFrame and GenomicRanges, serve as essential and foundational data structures, acting as the building blocks for extensive and complex representations. For example, container classes like SummarizedExperiment, SingleCellExperiment, and MultiAssayExperiment represent single or multi-omic experimental data and metadata.

For a complete list of packages, visit our GitHub organization.

Package	Description	PyPI	Links
BiocFrame	Bioconductor-like data frames		GitHub | Docs
IRanges	Interval arithmetic operations		GitHub | Docs | Bioconductor
GenomicRanges	Genomic location analysis		GitHub | Docs | Bioconductor

Package	Description	PyPI	Links
SummarizedExperiment	Genomic experiments container		GitHub | Docs | Bioconductor
SingleCellExperiment	Single-cell genomics container		GitHub | Docs | Bioconductor
SpatialExperiment	Spatial transcriptomics container		GitHub | Docs | Bioconductor
SpatialFeatureExperiment	Extends Spatial transcriptomics container		GitHub | Docs | Bioconductor
MultiAssayExperiment	Multi-omics data framework		GitHub | Docs | Bioconductor

Package	Description	PyPI	Links
txdb	Genome annotations from TxDB objects		GitHub | Docs
orgdb	Access OrgDb objects		GitHub | Docs
ensembldb	Access EnsDb objects		GitHub | Docs
experimenthub	Experiment data from ExperimentHub		GitHub | Docs
pyexpressionatlas	Experiment data from Expression Atlas		GitHub | Docs

Package	Description	PyPI	Links
rds2py	Read RDS files directly in Python		GitHub | Docs
BiocUtils	Common utilities mirroring R's base functionality		GitHub | Docs
mopsy	Matrix operations with R-like syntax		GitHub | Docs
pyBiocFileCache	Resource caching system		GitHub | Docs | Bioconductor

Package	Description	PyPI	Links
DelayedArray	Delayed operations in Python		GitHub | Docs | Bioconductor
HDF5Array	HDF5-backed arrays		GitHub | Docs | Bioconductor
TileDBArray	TileDB-backed arrays		GitHub | Docs | Bioconductor

All packages in the BiocPy are published to BiocPy PyPI org. Install the core packages using the biocpy wrapper:

pip install biocpy

Individual packages can be installed separately. See each package's documentation for specific installation instructions.

We provide conda/mamba configuration files to create environments containing most BiocPy (& friends) packages. Check out the environments repository for more information.

BiocPy integrates with several analysis tools and frameworks

• libscran: Multi-model single-cell analysis in R, Python and JavaScript.
• SingleR-inc: Cell type annotation for single-cell data.

• ArtifactDB: Language-agnostic access to data across computational environments.
• tatami-inc: Read various matrix representations through a common interface.

• CellArr: TileDB-based genomic data storage with AI/ML dataloaders.

We welcome contributions! Check out our developer guide to get started.