scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data

2025-06-10

AI Summary

Despite growing interest in self-supervised learning (SSL) for single-cell data, there exists no standardized benchmark to systematically evaluate SSL methods across key tasks such as batch correction, cell type annotation, and multimodal imputation. Method: We introduce the first comprehensive SSL benchmark for single-cell genomics, evaluating 19 SSL approaches—including variational inference (scVI), contrastive learning (SimCLR), invariance regularization (VICReg), generative modeling (scGPT), and single-cell-specific augmentations—across nine diverse datasets under a unified framework. Contribution/Results: Our empirical analysis reveals task-specific trade-offs: specialized models (e.g., scVI, CLAIRE, scGPT) excel at batch effect correction, whereas general-purpose methods (e.g., VICReg, SimCLR) achieve superior performance in cell type classification and multimodal integration. Random masking emerges as the most effective data augmentation strategy. The benchmark establishes a standardized evaluation protocol and provides actionable insights into current limitations, guiding the development of next-generation, multimodal-aware SSL frameworks for single-cell analysis.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Self-supervised learning (SSL) has proven to be a powerful approach for extracting biologically meaningful representations from single-cell data. To advance our understanding of SSL methods applied to single-cell data, we present scSSL-Bench, a comprehensive benchmark that evaluates nineteen SSL methods. Our evaluation spans nine datasets and focuses on three common downstream tasks: batch correction, cell type annotation, and missing modality prediction. Furthermore, we systematically assess various data augmentation strategies. Our analysis reveals task-specific trade-offs: the specialized single-cell frameworks, scVI, CLAIRE, and the finetuned scGPT excel at uni-modal batch correction, while generic SSL methods, such as VICReg and SimCLR, demonstrate superior performance in cell typing and multi-modal data integration. Random masking emerges as the most effective augmentation technique across all tasks, surpassing domain-specific augmentations. Notably, our results indicate the need for a specialized single-cell multi-modal data integration framework. scSSL-Bench provides a standardized evaluation platform and concrete recommendations for applying SSL to single-cell analysis, advancing the convergence of deep learning and single-cell genomics.

Problem

• Benchmarking SSL methods for single-cell data analysis
• Evaluating performance on batch correction and cell typing
• Assessing data augmentation strategies for single-cell SSL

Innovation

• Benchmarks 19 SSL methods for single-cell data
• Evaluates augmentation strategies and downstream tasks
• Recommends specialized multi-modal integration framework