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

TL;DR

scpFormer is a transformer-based model trained on over 390 million cells that enables unified, panel-agnostic analysis of single-cell proteomics data, improving integration, clustering, and biomarker discovery.

Contribution

It introduces a novel continuous, sequence-anchored tokenization and open-vocabulary architecture for single-cell proteomics, enhancing data integration and biological interpretation.

Findings

Performs competitively in large-scale batch integration and clustering.

Enables in silico panel expansion for better biological manifold reconstruction.

Supports transfer learning to bulk-omics for applications like drug response prediction.

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.