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

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...

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Bibliographic Details
Published in:bioRxiv
Main Authors: Xiao, Chuxi, Chen, Yixin, Wei, Lei, Zhang, Xuegong
Format: Paper
Language:English
Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 17.11.2023
Cold Spring Harbor Laboratory
Edition:1.1
Subjects:
Bioinformatics
Biological analysis
Integration
ISSN:2692-8205, 2692-8205
Online Access:Get full text
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Summary: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.Competing Interest StatementThe authors have declared no competing interest.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2023.11.15.564963