JSNMFuP: a unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization

With the rapid advancement of sequencing technology, the increasing availability of single-cell multi-omics data from the same cells has provided us with unprecedented opportunities to understand the cellular phenotypes. Integrating multi-omics data has the potential to enhance the ability to reveal cellular heterogeneity. However, data integration analysis is extremely challenging due to the different characteristics and noise levels of different molecular modalities in single-cell data. In this paper, an unsupervised integration method (JSNMFuP) based on non-negative matrix factorization is proposed. This method integrates the information extracted from the latent variables of each omic through a consensus graph. High-dimensional geometrical structure is captured in the original data and biologically-related feature links across modalities are incorporated into the model using regularization terms. JSNMFuP can be utilized for data visualization and clustering, facilitating marker characterization and gene ontology enrichment analysis, providing rich biological insights for downstream analysis. The application on real datasets shows that JSNMFuP has superior performance in cell clustering. The factors are interpretable, making it an effective method for analyzing cell heterogeneity using single-cell multi-omics data.