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This project

Resource Link
Repository kbaran1998/fed-maxfuse
Issues Issue tracker
Documentation kbaran1998.github.io/fed-maxfuse

MaxFuse

Resource Link
Reference implementation shuxiaoc/maxfuse
Documentation maxfuse.readthedocs.io
Paper (open access) doi.org/10.1038/s41587-023-01935-0
Protein → gene conversion table protein_gene_conversion.csv

Two versions of upstream MaxFuse exist

The released PyPI package and the development snapshot archived in the repository (Archive/MaxFuse_devo/) differ algorithmically, notably in the nearest-neighbour metric used during propagation, while sharing a version string. If you benchmark against "MaxFuse", record the exact commit or package version. See Comparison with Centralized MaxFuse.

Upstream licensing

The reference implementation is distributed under the Stanford/Penn Academic Software License: academic non-commercial use only, no redistribution without written permission, plus a broad reverse-engineering and derivative-ownership clause. This is not an open-source licence, and its scope is the reason Fed-MaxFuse's own licensing status is currently unresolved. Check it before building on upstream code.

Core dependencies

Tool Use
scanpy Single-cell preprocessing
AnnData Annotated data structures
scikit-learn CCA, decomposition, metrics
leidenalg / python-igraph Community detection
SciPy Linear sum assignment, sparse SVD
PyNNDescent Approximate nearest neighbours
ZADU Unsupervised embedding metrics
Typer CLI
uv Dependency management

Comparable integration methods

Method Approach Notes
Seurat v3 Horizontal, anchor-based Strong with shared features; degrades under weak linkage
Harmony Batch correction Different problem; used before MaxFuse in multi-donor settings
LIGER iNMF Requires shared feature space
BindSC Bi-order integration Benchmarked in the MaxFuse paper
scGLUE Deep generative Comparable on strong linkage; far higher compute cost

Federated learning

Resource Relevance
Flower General FL framework — a candidate for a networked deployment
NVIDIA FLARE FL for healthcare
Kairouz et al. (2021), Advances and Open Problems in Federated Learning Survey; useful on threat models

Infrastructure

Resource Link
Delft AI Cluster (DAIC) doc.daic.tudelft.nl
Apptainer apptainer.org