Authors
Zhenlan Liang, Ruiqing Zheng, Huayu Tao, Yuxuan Chen, Min Li
Published in
IEEE transactions on computational biology and bioinformatics. Volume PP. Aug 06, 2026. Epub Aug 06, 2026.
Abstract
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.
PMID:
42560914
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.
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