Recent advancements in single-cell sequencing technologies have enabled researchers to identify cell subpopulations and their functional states with greater accuracy, thereby uncovering cellular heterogeneity. However, due to the heterogeneity across different single-cell multi-omics datasets and the intrinsic variability among cells, effectively integrating data from multiple molecular layers remains a significant challenge. To address this issue, a Single-cell Multi-level Synergistic Learning model (LoHi-SSL) is proposed, which integrates low-order and high-order information to achieve efficient multi-omics data fusion. LoHi-SSL consists of three key modules: low-order information learning, high-order information learning, and feature integration. The low-order information learning module focuses on addressing intra-omics cellular heterogeneity. It first extracts features from each omics dataset, then constructs a graph structure to capture intercellular relationships. A Graph Autoencoder is employed to extract local neighborhood information, effectively preserving intra-omics cellular similarity. The high-order information learning module is designed to eliminate cross-omics heterogeneity and align data in a unified latent representation space. To achieve this, multi-omics hypergraph learning is introduced to model complex cellular relationships across different omics, enhancing feature interactions.In the feature integration module, contrastive learning is utilized to guide the model in learning more discriminative feature representations by constructing positive and negative sample pairs. For different omics data from the same cell, LoHi-SSL encourages feature alignment within a shared latent space, reducing cross-omics heterogeneity. Meanwhile, for different cell types, a contrastive loss function is applied to increase the separation between their representations, thereby enhancing cellular distinguishability and achieving efficient single-cell multi-omics integration. Experimental results demonstrate that LoHi-SSL outperforms existing methods on six publicly available datasets, achieving superior performance in clustering tasks, particularly in terms of NMI (Normalized Mutual Information), ARI (Adjusted Rand Index), AMI (Adjusted Mutual Information), and ACC (Clustering Accuracy). Furthermore, robustness analysis shows that LoHi-SSL exhibits strong resistance to noise. Additionally, cell trajectory analysis using the latent representations learned by LoHi-SSL accurately reflects biological evolutionary pathways. In summary, LoHi-SSL provides an efficient and robust approach for single-cell multi-omics data integration, offering a powerful tool for studying cellular state transitions, heterogeneity, and regulatory mechanisms.
Xiong et al. (Thu,) studied this question.