Gradient-Guided Graph Contrastive Learning for Mass Spectrometry-Based Proteomics Clustering

Single-cell proteomic data generated by mass spectrometry-based technologies provide direct insights into cellular functional states and have become increasingly important for revealing cellular heterogeneity in complex biological systems. Accurate clustering of such data is essential for identifying functionally distinct cell subpopulations and understanding biological processes such as immune responses, tumor heterogeneity, and cell fate regulation. However, mass spectrometry-based single-cell proteomic data are often characterized by high dimensionality, measurement noise, technical bias, and complex nonlinear structures, which pose major challenges to conventional clustering methods. To address these issues, this study proposes a gradient-information-guided graph contrastive learning framework for single-cell proteomic clustering. The proposed method adaptively reconstructs intercellular relationship graphs through gradient-guided structure learning and introduces a gradient-weighted contrastive loss to alleviate the influence of false-negative samples. By better preserving similarity among biologically related cells, the framework learns more robust and biologically meaningful representations. Experimental results on multiple data sets demonstrate that the proposed method outperforms conventional clustering approaches and existing graph contrastive learning methods in terms of clustering accuracy, stability, and biological consistency. Overall, this work provides an effective framework for clustering mass spectrometry-based single-cell proteomic data and offers new insights into the application of graph contrastive learning in bioinformatics.