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

Area of Science:

• Bioinformatics
• Proteomics
• Computational Biology

Background:

• Single-cell proteomic data offers insights into cellular heterogeneity.
• Accurate clustering is crucial for understanding biological processes like immune responses and tumor heterogeneity.
• Challenges include high dimensionality, noise, and complex data structures in mass spectrometry-based single-cell proteomics.

Purpose of the Study:

• To develop an advanced framework for clustering mass spectrometry-based single-cell proteomic data.
• To address limitations of conventional clustering methods in handling complex single-cell proteomic data.
• To improve the accuracy, stability, and biological interpretability of cell subpopulation identification.

Main Methods:

• Proposes a gradient-information-guided graph contrastive learning framework.
• Employs adaptive reconstruction of intercellular relationship graphs via gradient-guided structure learning.
• Introduces a gradient-weighted contrastive loss to mitigate false-negative sample influence.

Main Results:

• The proposed framework learns robust and biologically meaningful cell representations.
• Demonstrates superior performance over conventional and existing graph contrastive learning methods.
• Achieves higher clustering accuracy, stability, and biological consistency across multiple datasets.

Conclusions:

• Presents an effective framework for clustering mass spectrometry-based single-cell proteomic data.
• Highlights the utility of graph contrastive learning in bioinformatics for single-cell analysis.
• Offers new insights into analyzing complex single-cell proteomic datasets for biological discovery.