ArticleNature methods2025
Benchmarking single-cell multi-modal data integrations.
Article in Nature methods, 2025. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- Plasticity of Non-Apoptotic Residual Tumor Cells After Neoadjuvant Immunochemotherapy: Epigenetic and Microenvironmental Determinants.Biomolecules · 2026Review
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- Artificial intelligence virtual extracellular vesicles (AIVEVs).Bioactive materials · 2026Review
- SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.Bioinformatics (Oxford, England) · 2026Article
- Integrating single-cell RNA sequencing with multi-omics to decode disease microenvironments.Molecular biology reports · 2026Review
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions.bioRxiv : the preprint server for biology · 2026Article
- Article
- Single-cell data integration across weakly linked modalities.PLoS computational biology · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- scMultiPreDICT: A single-cell predictive framework with transcriptomic and epigenetic signatures.bioRxiv : the preprint server for biology · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- Integrating and mapping single-cell transcriptomics across the entire gene expression space.Briefings in bioinformatics · 2026Article
- Single-Cell Transcriptomic Analysis of Macaque LGN Neurons Reveals Novel Subpopulations.Molecular neurobiology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recent advances have enabled the generation of both unpaired (separate profiling) and paired (simultaneous measurement) single-cell multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes and data quality. Here we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics.
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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.