Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
By Zhi-Jie Cao, Ge Gao. Nature Biotechnology, 2022
505 citations recorded by the source corpus.
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 construction over millions of cells, where GLUE was able to correct previous annotations. 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. Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f0a15046d70124ef81ed2b885cfbefefc638f25a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Malte D. Luecken
- Fabian J Theis
- R. Argelaguet
- Luke Zappia
- H. Schiller
- Anna C. Schaar
- F. Drost
- Christopher Lance
Citations in the corpus
- Best practices for single-cell analysis across modalities (2023, background): “Large isotype counts can be detected in outlier cells, which should then be removed.”
- Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “To facilitate usage, some of these methods (for example, DeepMAPS 62 , FigR 63 , GLUE 64 , scAI 65 and SOMatic 66 ) implement their own integration approach.”