Effective Integration of Single-Cell Multi-Omics Data Using Improved Network-Based Integrative Clustering with Multigraph Regularization

The purpose of integrating different omics data is to study cellular heterogeneity at the level of transcriptional regulation from different gene levels, which can effectively identify cell types and reveal the pathogenesis of Alzheimer's disease (AD) from two perspectives. However, implementing such algorithms faces challenges such as high data noise levels, increased dimensionality, and computational complexity. In this study, multigraph regularization constraints were introduced in the network-based integrative clustering algorithm (MGR-NIC) to remove redundant features and keep the geometry structures underlying the data by fusing two types of data (snRNA-seq and snATAC-seq) of glial cells from AD samples. The effectiveness of the MGR-NIC algorithm was validated using both simulation datasets and real datasets derived from various tissues. The MGR-NIC algorithm can improve clustering accuracy by selecting features that better represent the dataset's structure. The clustering results obtained with the MGR-NIC algorithm show strong consistency with the clustering results inherent to the published DLPFC dataset, while the classification results generated using the NIC algorithm often lead to cluster overlap when applied to the DLPFC dataset. We will use the same state-of-the-art algorithms for a comprehensive evaluation with our proposed MGR-NIC algorithm, including NIC, scAI, Multi-Omics Factor Analysis v2, and JSNMF. MGR-NIC is the most stable and reliable method, implying its robustness across different datasets and its reliability in yielding consistent and accurate results.