scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911613923098624 |
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| author | Zhou, Qifeng Yu, Lei Guo, Yuzhi Miao, Yuwei Ma, Hehuan Zhong, Wenliang Xu, Lin Huang, Junzhou |
| author_facet | Zhou, Qifeng Yu, Lei Guo, Yuzhi Miao, Yuwei Ma, Hehuan Zhong, Wenliang Xu, Lin Huang, Junzhou |
| contents | The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we introduce scpFormer, a transformer-based foundation model designed for single-cell proteomics. Pre-trained on over 390 million cells, scpFormer replaces standard index-based tokenization with a continuous, sequence-anchored approach. By combining Evolutionary Scale Modeling (ESM) with value-aware expression embeddings, it dynamically maps variable panels into a shared semantic space without artificial discretization. We demonstrate that scpFormer generates global cell representations that perform competitively in large-scale batch integration and unsupervised clustering. Moreover, its open-vocabulary architecture facilitates in silico panel expansion, assisting in the reconstruction of biological manifolds in sparse clinical datasets. Finally, this learned protein co-expression logic is transferable to bulk-omics tasks, supporting applications like cancer drug response prediction. scpFormer provides a versatile, panel-agnostic framework to facilitate scalable biomarker discovery and precision oncology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20003 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics Zhou, Qifeng Yu, Lei Guo, Yuzhi Miao, Yuwei Ma, Hehuan Zhong, Wenliang Xu, Lin Huang, Junzhou Quantitative Methods Artificial Intelligence Machine Learning The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we introduce scpFormer, a transformer-based foundation model designed for single-cell proteomics. Pre-trained on over 390 million cells, scpFormer replaces standard index-based tokenization with a continuous, sequence-anchored approach. By combining Evolutionary Scale Modeling (ESM) with value-aware expression embeddings, it dynamically maps variable panels into a shared semantic space without artificial discretization. We demonstrate that scpFormer generates global cell representations that perform competitively in large-scale batch integration and unsupervised clustering. Moreover, its open-vocabulary architecture facilitates in silico panel expansion, assisting in the reconstruction of biological manifolds in sparse clinical datasets. Finally, this learned protein co-expression logic is transferable to bulk-omics tasks, supporting applications like cancer drug response prediction. scpFormer provides a versatile, panel-agnostic framework to facilitate scalable biomarker discovery and precision oncology. |
| title | scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.20003 |