DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data

Area of Science:

• Computational Biology
• Bioinformatics
• Artificial Intelligence in Healthcare

Background:

• Single-cell omics data analysis is vital for understanding biological complexity in healthcare.
• Existing AI clustering methods struggle with data sparsity, noise, and limited modeling of cell state transitions.
• Generative Adversarial Networks (GANs) and contrastive learning show promise but have limitations in single-cell data analysis.

Purpose of the Study:

• To develop an advanced AI-driven clustering method for low-quality single-cell omics data.
• To address limitations in existing methods, including overfitting and inadequate representation of cell state dynamics.
• To enhance the accuracy and robustness of single-cell data clustering for improved biological insights.

Main Methods:

• Proposed a novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering (DGAN-MPCC) method.
• Utilized two independent GANs to enhance input and bottleneck layers for refined cell embeddings.
• Implemented a multi-positive contrastive framework to diversify supervisory signals and capture continuous cell state transitions.

Main Results:

• DGAN-MPCC demonstrated superior performance compared to existing methods on multiple real-world single-cell datasets.
• The dual-GAN approach effectively improved data quality and reduced overfitting.
• The multi-positive contrastive learning framework enhanced the modeling of cell type-specific features and continuous transitions.

Conclusions:

• DGAN-MPCC offers a robust and efficient solution for clustering low-quality single-cell omics data.
• The method provides a valuable tool for AI-driven decision-making in healthcare and biological research.
• Advances in AI clustering are crucial for unlocking the full potential of single-cell omics data.