scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data

Abstract

Cell clustering plays a crucial role in the analysis of single-cell multi-omics research. Despite many methods for multi-omics integrated clustering, challenges such as noise, data sparsity, and biointerpretability analysis hider effective clustering. Recent studies have demonstrated that anchor graph learning clustering in the multiview graph domain can help alleviate sparsity and high noise, reducing runtime costs. However, addressing the heterogeneity and high noise levels in multi-omics data is crucial for obtaining a more representative anchor graph. Furthermore, the existing methods often directly obtain surface information from multi-omics for clustering purposes, neglecting the mining and utilization of higher-order correlations among different features within shared information. In response to these challenges, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Extensive experiments show that our method not only outperforms 13 state-of-the-art methods in terms of clustering metrics and running time, but also that the completed omics retains the biological meaning of the original omics.