Federated-inspired Single-cell Batch Integration in Latent Space

2026-01-31

AI Summary

This study addresses the challenge of batch effects in single-cell RNA sequencing data, which often obscure true biological signals and hinder cross-experiment integration, particularly in distributed and continuously growing data settings where existing methods fall short. To this end, the authors propose scBatchProx, a federated learning–inspired post-processing optimization framework that introduces the principles of federated learning to single-cell batch correction for the first time. scBatchProx operates without requiring access to raw expression data or centralized retraining; instead, it performs lightweight refinement of cell embeddings produced by any upstream method in latent space, leveraging batch-conditioned adapters and proximal regularization for efficient correction. Evaluations demonstrate that scBatchProx significantly improves batch removal in 90% of data–method combinations while better preserving biological signals in 85%, yielding an overall 3–8% relative improvement in embedding quality.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Advances in single-cell RNA sequencing enable the rapid generation of massive, high-dimensional datasets, yet the accumulation of data across experiments introduces batch effects that obscure true biological signals. Existing batch correction approaches either insufficiently correct batch effects or require centralized retraining on the complete dataset, limiting their applicability in distributed and continually evolving single-cell data settings. We introduce scBatchProx, a post-hoc optimization method inspired by federated learning principles for refining cell-level embeddings produced by arbitrary upstream methods. Treating each batch as a client, scBatchProx learns batch-conditioned adapters under proximal regularization, correcting batch structure directly in latent space without requiring raw expression data or centralized optimization. The method is lightweight and deployable, optimizing batch-specific adapter parameters only. Extensive experiments show that scBatchProx consistently yields relative gains of approximately 3-8% in overall embedding quality, with batch correction and biological conservation improving in 90% and 85% of data-method pairs, respectively. We envision this work as a step toward the practical refinement of learned representations in dynamic single-cell data systems.

Problem

• batch effect
• single-cell RNA sequencing
• distributed data
• latent space
• biological signal

Innovation

• federated learning
• batch effect correction
• latent space
• single-cell RNA sequencing
• post-hoc optimization