SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data
npj Systems Biology and Applications, vol. 12
Abstract
Rapid technological progress now enables large-scale generation of single-cell data. Many laboratories can produce single-cell transcriptomic profiles from diverse tissues. A key step in single-cell analysis is unsupervised clustering followed by cell-type annotation, yet there is no agreement on marker genes, and annotation is typically done manually, making it irreproducible and poorly scalable. Privacy constraints in human datasets further complicate data sharing. There is a need for standardized, automated, and privacy-preserving cell-type annotation across datasets. We developed SwarmMAP, which applies Swarm Learning to train machine-learning models for cell-type classification in a decentralized setting without exchanging raw data between centers. SwarmMAP achieves F1-scores of 0.93, 0.98, and 0.88 in heart, lung, and breast datasets, respectively. Swarm Learning models reach an average performance of 0.907, comparable to models trained on centralized data (p-val = 0.937, Mann-Whitney U Test). Increasing the number of datasets improves prediction accuracy and supports classification across broader cell-type diversity. These results show that Swarm Learning provides an effective approach for automated cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP .
Authors 7
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Technische Universität Dresden · University Hospital Heidelberg · Else Kröner Fresenius Center for Digital Health · National Center for Tumor Diseases
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Affiliation as printed
Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, North Rhine-Westphalia, Germany
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Affiliation as printed
Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, North Rhine-Westphalia, Germany
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Technische Universität Dresden · University Hospital Heidelberg · Else Kröner Fresenius Center for Digital Health · National Center for Tumor Diseases
Affiliation as printed
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Affiliation as printed
Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, North Rhine-Westphalia, Germany
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RWTH Aachen University · Icahn School of Medicine at Mount Sinai
Affiliation as printed
Cardiovascular Research Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. sikander.hayat@mssm.edu
Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, North Rhine-Westphalia, Germany. sikander.hayat@mssm.edu
Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. sikander.hayat@mssm.edu
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Jakob Nikolas Kather corresponding
Technische Universität Dresden · University Hospital Heidelberg · Else Kröner Fresenius Center for Digital Health · University Hospital Carl Gustav Carus · National Center for Tumor Diseases
Affiliation as printed
Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Saxony, Germany. jakob_nikolas.kather@tu-dresden.de
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Saxony, Germany. jakob_nikolas.kather@tu-dresden.de
Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Baden-Wuerttemberg, Germany. jakob_nikolas.kather@tu-dresden.de
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