Open World Knowledge Aided Single-Cell Foundation Model with Robust Cross-Modal Cell-Language Pre-training

• URL: http://arxiv.org/abs/2601.05648v1
• Date: Fri, 09 Jan 2026 09:10:14 GMT
• Title: Open World Knowledge Aided Single-Cell Foundation Model with Robust Cross-Modal Cell-Language Pre-training
• Authors: Haoran Wang, Xuanyi Zhang, Shuangsang Fang, Longke Ran, Ziqing Deng, Yong Zhang, Yuxiang Li, Shaoshuai Li,
• Abstract summary: We propose an Open-world Language Knowledge-Aided Robust Single-Cell Foundation Model (OKR-CELL)<n>It is built based on a cross-modal Cell-Language pre-training framework, which comprises two key innovations.<n>OKR-CELL obtains cutting-edge results across 6 evaluation tasks.
• Score: 7.812507078660317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in single-cell multi-omics, particularly RNA-seq, have provided profound insights into cellular heterogeneity and gene regulation. While pre-trained language model (PLM) paradigm based single-cell foundation models have shown promise, they remain constrained by insufficient integration of in-depth individual profiles and neglecting the influence of noise within multi-modal data. To address both issues, we propose an Open-world Language Knowledge-Aided Robust Single-Cell Foundation Model (OKR-CELL). It is built based on a cross-modal Cell-Language pre-training framework, which comprises two key innovations: (1) leveraging Large Language Models (LLMs) based workflow with retrieval-augmented generation (RAG) enriches cell textual descriptions using open-world knowledge; (2) devising a Cross-modal Robust Alignment (CRA) objective that incorporates sample reliability assessment, curriculum learning, and coupled momentum contrastive learning to strengthen the model's resistance to noisy data. After pretraining on 32M cell-text pairs, OKR-CELL obtains cutting-edge results across 6 evaluation tasks. Beyond standard benchmarks such as cell clustering, cell-type annotation, batch-effect correction, and few-shot annotation, the model also demonstrates superior performance in broader multi-modal applications, including zero-shot cell-type annotation and bidirectional cell-text retrieval.

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