ScCLC: A Flexible Contrastive Learning Framework for Single-cell Multi-omics Data Clustering.

The rapid development of single-cell joint profiling technologies enables the simultaneous measurement of multiple molecular modalities from the same cell, providing unprecedented opportunities to characterize cellular heterogeneity. However, effectively integrating heterogeneous and high-dimensional multi-omics data remains a fundamental challenge for accurate cell clustering. In this work, we propose scCLC, a topology-aware contrastive learning framework for clustering single-cell multi-omics data. scCLC adopts contrastive learning as the backbone for cell representation learning and introduces a dedicated multi-view data augmentation strategy to address modality-specific characteristics. By exploiting the intrinsic cell-cell topological structures constructed from multi-omics data, scCLC identifies informative positive pairs for self-supervised training, which encourages the learned representations to be more cluster-discriminative. Extensive experiments on multiple paired datasets demonstrate the effectiveness of scCLC for clustering single-cell multi-omics data. Visualization analyses further indicate that scCLC is capable of distinguishing rare cell populations in highly imbalanced datasets. Moreover, case studies on single-cell triple-omics datasets illustrate that scCLC can be readily extended to integrate additional modalities, underscoring its flexibility and scalability for multi-omics data analysis. The source code can be downloaded from https://github.com/CSUBioGroup/scCLC.