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arXiv · 2604.20003

scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics

Qifeng Zhou·Lei Yu·Yuzhi Guo·Yuwei Miao·Hehuan Ma·Wenliang Zhong·Lin Xu·Junzhou Huang
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Abstract

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.

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Qifeng Zhou, Lei Yu, Yuzhi Guo, Yuwei Miao, Hehuan Ma, Wenliang Zhong, Lin Xu, Junzhou Huang. 2026-04-21. scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics. https://arxiv.org/abs/2604.20003

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