mudataoom 0.2.0

Out-of-memory MuData — drop-in replacement for mudata.MuData built on mudata-rs and anndataoom

mudata-oom

Out-of-memory MuData powered by anndataoom.

Drop-in replacement for mudata.MuData whose per-modality expression matrices stay on disk. Every API method, every property, every behaviour of upstream mudata.MuData is preserved — the only difference is that the modalities are anndataoom.AnnDataOOM objects instead of anndata.AnnData, so reading and processing million-cell multimodal atlases doesn't blow up your RAM.

import mudataoom as moom
mdata = moom.read_h5mu("cite_seq_1M.h5mu")
# MuDataOOM [out-of-memory · backed]
#   mod
#     rna  : AnnDataOOM  1,200,000 × 24,000
#     prot : AnnDataOOM  1,200,000 × 156
#   obs  : 1,200,000 × 5
#   obsm : ['X_mofa']

Why?

mudata.MuData is fine for the joint metadata (a few MB), but each modality is a full AnnData that loads its X into RAM. For a 1M-cell CITE-seq atlas (RNA + protein), this means >100 GB. mudataoom keeps each modality's X on disk and runs single-modality preprocessing (normalize, log1p, scale, PCA) through anndataoom's lazy / chunked operator chain.

Dataset	mudata.MuData	mudataoom	Saving
CITE-seq 10k (RNA + 156 ADT)	2.4 GB	~70 MB	34x
RNA + ATAC 100k cells	~24 GB	~1.2 GB	20x
RNA + ATAC 1M cells	~140 GB (OOM)	~1.7 GB	80x+

Architecture (no new Rust code)

MuDataOOM (Python; subclasses mudata.MuData)
├── _mod: OrderedDict[str, AnnDataOOM]   # per-modality; X stays on disk
├── _obs, _var: pd.DataFrame             # joint metadata
├── _obsm, _varm, _obsp, _varp           # joint embeddings / graphs
├── _obsmap, _varmap                     # which modality row backs which joint row
├── _uns, _axis
└── _file: MuDataFileManager             # h5mu HDF5 file handle (backed mode)

read_h5mu walks /mod/* inside the source .h5mu, builds one tiny "virtual" .h5ad per modality whose datasets are HDF5 ExternalLinks back into the source file — no copying of X — then opens each virtual .h5ad through anndataoom.read(). The Rust I/O layer streams X chunks straight from the original .h5mu on demand.

See docs/architecture.md for the full design.

Install

pip install mudataoom

(Will pull anndataoom, mudata, h5py, anndata.)

Compatibility

MuDataOOM is a strict subclass of mudata.MuData. Anything that takes a mudata.MuData (mofapy, muon plotting, scvi-tools, …) accepts a MuDataOOM unchanged.

License

MIT. See LICENSE.