Towards a knowledge-enhanced single-cell foundation model

2026-09-13

AI Summary

研究通过结合细胞注释和基因调控信息，提出scKITE模型，以更少的预训练样本提高单细胞基础模型性能。

Abstract

Single-cell foundation models (scFMs) increasingly rely on large-scale transcriptomic pretraining, yet expanding pretraining data can yield diminishing gains while substantially increasing computational cost. Our data scaling analyses showed that incorporating biological knowledge, including cell-level text annotation and gene-level regulatory information, provided additional scaling dimension than simply increasing data size. Motivated by this observation, we present scKITE, a simple yet effective scFM that integrates cell-annotation and gene-regulatory supervision into a shared transcriptomic Transformer encoder through lightweight auxiliary decoders. These decoders are used only during pretraining and subsequently discarded, yielding a general-purpose encoder enriched with biological knowledge for downstream applications. With only 179,067 pretraining samples, i.e., less than 0.5\% of those used by previous strong scFMs, scKITE outperformed these models across diverse downstream tasks, highlighting knowledge-enhanced pretraining as a promising paradigm for biologically grounded scFMs.

Problem

• single-cell foundation models
• transcriptomic pretraining
• biological knowledge
• data scaling

Innovation

• knowledge-enhanced pretraining
• biological knowledge integration
• lightweight auxiliary decoders