Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding framework

Abstract

With the ever-increasing amount of single-cell multi-omics data accumulated during the past years, effective and efficient computational integration is becoming a serious challenge. One major obstacle of unpaired multi-omics integration is the feature discrepancies among omics layers. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which utilizes accessible prior knowledge about regulatory interactions to bridge the gaps between feature spaces. Systematic benchmarks demonstrated that GLUE is accurate, robust and scalable. We further employed GLUE for various challenging tasks, including triple-omics integration, model-based regulatory inference and multi-omics human cell atlas construction (over millions of cells) and found that GLUE achieved superior performance for each task. As a generalizable framework, GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE for the community.