SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.

Area of Science:

• Computational Biology
• Genomics
• Machine Learning

Background:

• Single-cell transcriptomic data generation is rapidly advancing, enabling large-scale tissue analysis.
• Current cell-type annotation relies on manual inspection of marker genes, which is inconsistent and difficult to scale.
• Patient privacy is a significant concern for human single-cell datasets.

Purpose of the Study:

• To develop a standardized and automated method for cell-type annotation of single-cell sequencing data.
• To address privacy concerns by enabling decentralized analysis without raw data exchange.
• To evaluate the performance of a novel Swarm Learning-based approach for cell-type classification.

Main Methods:

• Developed SwarmMAP, a tool utilizing Swarm Learning for decentralized machine learning model training.
• Trained models on single-cell sequencing data from human heart, lung, and breast tissues.
• Compared performance of decentralized Swarm Learning models against centralized training.

Main Results:

• SwarmMAP achieved high F1-scores: 0.93 (heart), 0.98 (lung), and 0.88 (breast).
• Swarm Learning models showed an average performance of 0.907, comparable to centralized models (p-val=0.937).
• Increased number of datasets improved prediction accuracy and cell-type diversity handling.

Conclusions:

• Swarm Learning provides a viable, privacy-preserving approach for automating cell-type annotation in single-cell analyses.
• SwarmMAP demonstrates the potential of decentralized learning for scalable and reproducible single-cell data analysis.
• The methodology facilitates robust cell-type classification across diverse datasets.