scEAGC: an efficient anchor graph clustering for single-cell transcriptomics and proteomics data

Single-cell multi-omics sequencing allows researchers to simultaneously sequence multiple types of molecular information from the same individual cell, like transcriptomics and proteomics. However, the research of identifying cell types from single-cell multi-omics data is still challenging. In this article, we proposed scEAGC, an efficient anchor graph clustering for single-cell transcriptomics and proteomics data. It first constructs anchor cell graphs for every omics and then integrates separated omics-specific anchor cell graphs on a weighted multi-view clustering model with F-norm and the Orthogonal constraint, finally through the divided iterative optimization method to obtain cluster partition without any extra post-processing. Since scEAGC combines the high-efficiency property of anchor graph clustering, its efficiency is substantially higher than widely used algorithms. Extensive experiments demonstrate that, compared to other state-of-the-art clustering algorithms, scEAGC can boost clustering accuracy and robustness and detect the new cell subtypes in CITE-seq and scRNA-seq data.