Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder

Abstract Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more compre...

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Bibliographic Details
Published in:Briefings in bioinformatics 2024-07, Vol.25 (5)
Main Authors: Lv, Tongxuan, Zhang, Yong, Liu, Junlin, Kang, Qiang, Liu, Lin
Format: Article
Language:English
Subjects:
Algorithms
Biological analysis
Coders
Computational Biology - methods
Data analysis
Heterogeneity
Humans
Information processing
Integration
Multiomics
Problem Solving Protocol
Proteomics
Proteomics - methods
Self-supervised learning
Single-Cell Analysis - methods
Spatial data
Spatial discrimination learning
Spatial heterogeneity
Transcriptome
Transcriptomics
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Summary:Abstract Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae450