ScMGCL: accurate and efficient integration representation of single-cell multi-omics data.

ABSTRACT:

Motivation

Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell-cell similarity graphs, in which each modality's graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features.

Results

Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and

SUBMITTER: Cheng Z 

PROVIDER: S-EPMC12308176 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature