Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data

Abstract Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehen...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 25; no. 2
Main Authors: Xiao, Chuxi, Chen, Yixin, Meng, Qiuchen, Wei, Lei, Zhang, Xuegong
Format: Journal Article
Language:English
Published: England Oxford University Press 22.01.2024
Oxford Publishing Limited (England)
Subjects:
Algorithms
Biological analysis
Cell Cycle
Cellular communication
Data analysis
Data integration
Heterogeneity
Integration
Multiomics
Review
RNA
Software
multi-omics
integration
single cell
benchmarking
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration.
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Chuxi Xiao and Yixin Chen contributed equally to this work.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae095