mudata
Multimodal data
Decision gist · record as of 2026-08-14
Yes. MuData is actively maintained, has no known vulnerabilities, and fills a clear role in the scverse ecosystem for multimodal omics data. The low install friction, permissive license, and integration with established tools like anndata and muon make it a straightforward choice if you work with multimodal single-cell data. Install it if you need to organize or persist data from multiple assays measured on the same cells.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Assumes anndata objects (adata_rna, adata_atac) are already available.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause permissive license allows commercial and private use with attribution and liability disclaimer. No restrictions on modification or redistribution.
last release 2026-07-07 (38 days) · last repo commit 2026-08-14 · 130 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 194,170 downloads/mo, #9,844 on PyPI
Alternatives
Verify before relying
pip install mudata
from mudata import MuData
mdata = MuData({'rna': adata_rna, 'atac': adata_atac})
mdata.write('multimodal.h5mu')
mdata = MuData.read('multimodal.h5mu')- Whether the package handles edge cases like mismatched observation counts across modalities gracefully.
- Performance characteristics when working with very large multimodal datasets (millions of cells, thousands of features per modality).
- Interoperability with tools outside the scverse ecosystem for reading/writing .h5mu files.
What it is and what it does
MuData extends the AnnData single-cell data structure to handle multimodal omics experiments where multiple assays (RNA, ATAC, protein, etc.) are measured on the same cells. It stores each modality as a separate AnnData object within a unified container, allowing shared cell and feature metadata while preserving modality-specific annotations. The package provides HDF5-based persistence through .h5mu files, which mirror the .h5ad format but support hierarchical storage of multiple modalities.
Typically used in single-cell genomics workflows where data from technologies like 10X Genomics multiome (RNA + ATAC) or other multi-assay platforms need to be loaded, processed, and stored together. The package is part of the scverse ecosystem and integrates with muon for convenient readers of common multimodal file formats. It depends on anndata, h5py, numpy, pandas, and scipy for core functionality.
Use it for
- Store and retrieve 10X Genomics multiome data (RNA + ATAC) with shared cell metadata and modality-specific feature annotations.
- Combine multiple single-cell assays measured on the same cells into one analysis-ready container with linked observations.
- Save multimodal analysis results to disk in a structured HDF5 format and selectively read individual modalities back.
- Organize metadata for multimodal experiments where cell-level annotations apply across all assays but feature annotations differ per modality.
- Build reproducible multimodal analysis pipelines that preserve data structure and metadata through multiple processing steps.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MuData is actively maintained, has no known vulnerabilities, and fills a clear role in the scverse ecosystem for multimodal omics data. The low install friction, permissive license, and integration with established tools like anndata and muon make it a straightforward choice if you work with multimodal single-cell data. Install it if you need to organize or persist data from multiple assays measured on the same cells.
Install
mudata on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a release 38 days ago and recent commits. Seven runtime dependencies are all established scientific Python packages (anndata, h5py, numpy, pandas, scipy, scverse-misc, session-info2).
Requires Python 3.10 or later. Assumes anndata objects (adata_rna, adata_atac) are already available.
License in practice
BSD 3-Clause permissive license allows commercial and private use with attribution and liability disclaimer. No restrictions on modification or redistribution.
Quickstart
pip install mudata
from mudata import MuData
mdata = MuData({'rna': adata_rna, 'atac': adata_atac})
mdata.write('multimodal.h5mu')
mdata = MuData.read('multimodal.h5mu')Verify before relying
- Whether the package handles edge cases like mismatched observation counts across modalities gracefully.
- Performance characteristics when working with very large multimodal datasets (millions of cells, thousands of features per modality).
- Interoperability with tools outside the scverse ecosystem for reading/writing .h5mu files.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesanndatah5pynumpypandasscipyscverse-miscsession-info2 |
| Maintenance | Actively maintained 38 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 194,170 / month, #9,844 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Bio-Informatics |
Evidence: mudata-0.3.10-py3-none-any.whl
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