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Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.
Tianyu Liu1,2, Kexing Li1,2, Yuge Wang2
1Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, Connecticut, USA.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 23, 2026
Summary
Foundation Models (FMs) show promise in single-cell sequencing analysis. However, they do not consistently outperform specialized methods, challenging their necessity and guiding future development.
Area of Science:
- Computational Biology
- Genomics
- Artificial Intelligence
Background:
- Foundation Models (FMs) are increasingly used in scientific research.
- Single-cell sequencing data analysis is a complex field with growing data volumes.
Purpose of the Study:
- To comprehensively evaluate Foundation Models for single-cell sequencing data analysis.
- To compare FM performance against task-specific methods and assess their necessity.
- To provide guidelines for training and fine-tuning single-cell FMs.
Main Methods:
- Experimental evaluation of ten single-cell FMs across eight downstream tasks.
- Comparison of FM performance with established task-specific methods.
- Analysis of hyperparameter effects, initial settings, and training stability using the scEval framework.
Main Results:
- scGPT, Geneformer, and CellFM identified as top-performing FMs based on performance and accessibility.
- Single-cell FMs did not consistently outperform task-specific methods across all evaluated tasks.
- Identified key factors influencing single-cell FM training and performance.
Conclusions:
- The consistent superiority of single-cell FMs over task-specific methods is not yet established, questioning their universal necessity.
- Guidelines for pre-training and fine-tuning are provided to enhance single-cell FM performance.
- An open-source evaluation pipeline is offered for benchmarking and advancing single-cell analysis methods.
