ArticleNature methods2025
Multitask benchmarking of single-cell multimodal omics integration methods.
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 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
21 citing papers in PubMed.
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Clonal Metamorphosis: Deconstructing MPN Evolution with Single-Cell and Spatial Multi-Omics.Clinical and experimental medicine · 2026Review
- MagmaFlow: A desktop platform for artificial intelligence-driven expression analysis.FEBS open bio · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.Science advances · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Substance abuse and inflammation: consequences beyond the brain.Trends in immunology · 2026Review
- Coming to light: the transcriptional regulatory roles of histone lysine crotonylation in health and disease.Cellular & molecular biology letters · 2026Review
- 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
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Context-dependent immune regulation: a mechanistic and AI-enabled integrative framework.Frontiers in immunology · 2026Review
- BLIT: an R package for seamless integration of command-line bioinformatics tool universe.Bioinformatics advances · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.Briefings in bioinformatics · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Securing diagonal integration of multimodal single-cell data against ambiguous mapping.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Single-cell multimodal omics technologies have empowered the profiling of complex biological systems at a resolution and scale that were previously unattainable. These biotechnologies have propelled the fast-paced innovation and development of data integration methods, leading to a critical need for their systematic categorization, evaluation and benchmarking. Navigating and selecting the most pertinent integration approach poses a considerable challenge, contingent upon the tasks relevant to the study goals and the combination of modalities and batches present in the data at hand. Understanding how well each method performs multiple tasks, including dimension reduction, batch correction, cell type classification and clustering, imputation, feature selection and spatial registration, and at which combinations will help guide this decision. Here we develop a much-needed guideline on choosing the most appropriate method for single-cell multimodal omics data analysis through a systematic categorization and comprehensive benchmarking of current methods. The stage 1 protocol for this Registered Report was accepted in principle on 30 July 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Multi-task_benchmarking_of_single-cell_multimodal_omics_integration_methods/26789902 .
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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.