Multi-omics single-cell data integration and regulatory inference with graph-linked embedding.

ABSTRACT: Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas

SUBMITTER: Cao ZJ 

PROVIDER: S-EPMC9546775 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature