CytoBERT：用于细胞计数数据的基础模型
CytoBERT: A Foundation Model for Cytometry Data
- University of Rostock（罗斯托克大学）
- Marburg University（马尔堡大学）
- Hessian Center for Artificial Intelligence（黑森人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对细胞计数数据异质性与非标准化导致机器学习难以应用的问题，推出开源基础模型CytoBERT，经5000万细胞数据预训练后可实现跨数据集迁移学习，为通用细胞计数分析提供新方案。
AI中文摘要：
细胞计数技术可测量单细胞的复杂特征，例如免疫细胞的数量和蛋白表达，广泛应用于免疫学研究和临床场景。然而，由于实验方案和测量特征选择的差异，细胞计数数据具有高度异质性和非标准化的特点。尽管机器学习方法有望深入洞察细胞生物学，但这些挑战使其难以在不同研究中应用和迁移。近期基础模型的进展可缓解这些问题，但该领域的相关方法仍较为匮乏。为解决此问题，我们推出CytoBERT，这是一款公开可用、开源、开放权重的基础模型，适用于具有可变标记 panel 的单细胞细胞计数数据。CytoBERT 通过自监督方式在大规模细胞计数语料库上进行预训练，该语料库包含15个人类数据集，具有异质性标记 panel 和超过5000万个细胞，且通过标记标准化进行整理，使其能够学习细胞内可迁移的标记间关系。将CytoBERT微调用于样本级分类，证明了跨异质性细胞计数数据集的迁移学习是可行的，为可扩展、可泛化的细胞计数分析提供了起点，代码可在GitHub获取。
英文摘要：
Cytometry measures the complex characteristics of single cells (e.g., counts and protein expression of immune cells) and is widely used across immunological research and clinical settings. However, cytometry data is highly heterogeneous and unstandardized due to experimental protocols and the choice of measured features. While machine learning methods hold the potential to gain deeper insights into cell biology, these challenges make them difficult to apply and transfer across studies. Recent advances in foundation models can alleviate these issues, but corresponding approaches are still scarce in this field. To address this, we provide CytoBERT, a publicly available, open-source, open-weight foundation model for single-cell cytometry data with variable marker panels. CytoBERT is pretrained in a self-supervised manner on a large-scale cytometry corpus (15 human datasets with heterogeneous marker panels and more than 50 million cells) curated through marker standardization, enabling it to learn transferable inter-marker relationships within cells. Fine-tuning CytoBERT for sample-level classification demonstrates that transfer learning across heterogeneous cytometry datasets is feasible, providing a starting point for scalable, generalizable cytometry analysis. Code is available at GitHub.
