Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Cluster analysis is crucial for single-cell multi-omics research, enabling the identification of cellular diversity and states.
• Current methods struggle with multi-omics data complexity, integration across dimensions, and dynamic adaptability.
• Existing approaches may overemphasize local details, neglect global patterns, and lack flexible clustering objectives.

Purpose of the Study:

• To introduce scHEAGC, a novel hybrid encoding and adaptive guidance framework for enhanced single-cell multi-omics clustering.
• To address limitations in existing methods regarding data complexity, integration, and adaptability.
• To improve the representation and analysis of single-cell multi-omics data.

Main Methods:

• Employs a hybrid encoding strategy using Vector Quantization-Variational Autoencoders (VQ-VAEs) and Graph Autoencoders (GAEs).
• Features a multi-layer information fusion strategy for robust integration of multi-dimensional data at local and global levels.
• Incorporates an adaptive guided clustering module for dynamic target adjustment and iterative optimization.

Main Results:

• scHEAGC demonstrates superior performance compared to existing methods across six real-world single-cell multi-omics datasets.
• The hybrid encoding and adaptive guided information fusion strategies effectively capture data complexity and diversity.
• The framework successfully integrates information across omics levels, improving cell type differentiation.

Conclusions:

• scHEAGC provides a powerful new tool for single-cell multi-omics data analysis, overcoming limitations of current clustering methods.
• The proposed framework is effective for enhancing clustering accuracy and data representation in multi-omics studies.
• The hybrid encoding and adaptive guided information fusion strategies are extendable to other data analysis fields.