scMCs: a framework for single-cell multi-omics data integration and multiple clusterings

Liangrui Ren, Jun Wang, Zhao Li, Qingzhong Li, Guoxian Yu

Abstract: The integration of single-cell multi-omics data can uncover the underlying regulatory basis of diverse cell types and states. However, contemporary methods disregard the omics individuality, and the high noise, sparsity, and heterogeneity of single-cell data also impact the fusion effect. Furthermore, available single-cell clustering methods only focus on the cell type clustering, which cannot mine the alternative clustering to comprehensively analyze cells.