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.
@article{arxiv.2604.20003,
title = {scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics},
author = {Qifeng Zhou and Lei Yu and Yuzhi Guo and Yuwei Miao and Hehuan Ma and Wenliang Zhong and Lin Xu and Junzhou Huang},
journal= {arXiv preprint arXiv:2604.20003},
year = {2026}
}