流式细胞数据太杂不好统一？CytoBERT在5000万细胞上预训练，换数据集微调即用，避免从头训练。
CytoBERT是一个开源的细胞计数数据基础模型，支持可变标记面板。它在15个人类数据集、超过5000万个细胞上自监督预训练。通过标记标准化，模型学会跨数据集的细胞间标记关系。在样本级分类任务上微调后，证明了跨异质细胞计数数据集的迁移学习可行。代码已公开在GitHub。
CytoBERT: A Foundation Model for Cytometry Data
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.