Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization

Recent advancements in single-cell multi-omics technologies have significantly improved our ability to explore cellular heterogeneity at an unprecedented resolution. These innovations enable the simultaneous profiling of genomic, transcriptomic, proteomic, and epigenetic data at the single-cell level, providing comprehensive insights into cellular states and their regulatory mechanisms. However, integrating multi-omics data remains challenging due to its high dimensionality, technical noise, and biological complexity. To address these challenges, we introduce scWDAC (single-cell weighted distance adaptive clustering), a novel clustering method for single-cell multi-omics data. scWDAC utilizes a weighted distance penalty and adaptive graph regularization to effectively integrate multiple omics layers. Key innovations of scWDAC include using a weighted distance penalty to capture cell-to-cell similarities across different omics modalities, and applying adaptive graph regularization on a consensus matrix to enforce cross-modal consistency. The framework optimizes both global consistency and local accuracy, ensuring a robust exploration of cellular structures across all omics layers. The effectiveness of scWDAC is evaluated through extensive experiments on ten paired single-cell multi-omics datasets. The results demonstrate that scWDAC outperforms existing clustering methods in terms of clustering accuracy, robustness to noise, and biological interpretability.Author summary: Recent advancements in single-cell high-throughput technology have transformed single-cell multi-omics, enabling researchers to study cellular heterogeneity with high resolution. This progress has significantly improved our understanding of complex biological systems and diseases. Single-cell multi-omics data, including genomics, transcriptomics, proteomics, and epigenetics, provides a comprehensive view of cellular states and gene regulatory mechanisms. However, its analysis remains challenging due to high dimensionality, noise, and complexity. To address these challenges, we present scWDAC, a novel clustering algorithm for single-cell multi-omics data. scWDAC integrates omics layers by employing weighted distance penalties and adaptive graph regularization. It captures cell-to-cell similarities across layers, minimizes low-rank approximations, and enforces cross-modal consistency through consensus matrix regularization. By optimizing both global consistency and local accuracy, scWDAC uncovers robust cellular structures.