ArticleBriefings in bioinformatics2024
Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Spatial and Temporal Heterogeneity of Macrophage Efferocytosis in Tumors: Emerging Implications for Immunotherapy Biomarkers and Therapeutic Timing.Cancer medicine · 2026Review
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Article
- Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.Science advances · 2026Article
- Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.bioRxiv : the preprint server for biology · 2026Article
- GAMMI: graph-guided contrastive and adversarial integration of single-cell and spatial multi-omics data.Briefings in bioinformatics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Dissecting Normal and Abnormal Human Kidney Development Using Multiomics.Journal of the American Society of Nephrology : JASN · 2026Review
- BiCLUM: Bilateral contrastive learning for unpaired single-cell multi-omics integration.PLoS computational biology · 2026Article
- Decoding Vascular Cell Diversity: Single-Cell Approaches to Mechanisms of Vascular Disease.Circulation research · 2026Review
- Clocking immunity: circadian modulation of NK cells and emerging timing perspectives.Frontiers in immunology · 2026Review
- Critical assessment in the age of AI: challenges and opportunities for computational biology.Bioinformatics advances · 2026Article
- Benchmarking deep learning methods for biologically conserved single-cell integration.Genome biology · 2025Article
- Assessment and applications of joint profiling of single-cell chromatin accessibility and transcriptome.Briefings in bioinformatics · 2025Review
- SuperGLUE facilitates an explainable training framework for multi-modal data analysis.Cell reports methods · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.