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

ABSTRACT:

Motivation

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.

Results

We propose a single-cell data fusion based multiple clustering (scMCs) approach that can jointly model single-cell transcriptomics and epigenetic data, and explore multiple different clusterings. scMCs first mines the omics-specific and cross-omics consistent representations, then fuses them into a co-

SUBMITTER: Ren L 

PROVIDER: S-EPMC10070040 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature