ArticleNature communications2021
Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 1 of them a synthesis that pooled it.
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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
114 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2025Pooled it
- Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome.Frontiers in immunology · 2026Trial
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Harnessing the microbiome: a new frontier in lung cancer immunotherapy.Cancer biology & medicine · 2026Review
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- VDJ-REMIX: REpertoire Module Identification and eXploration.Bioinformatics (Oxford, England) · 2026Article
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Local graph estimation with pathwise false discovery control.Nature communications · 2026Article
- CEDR: robust consensus cancer subtyping with multi-omics data via ensemble dimensionality reduction.Briefings in bioinformatics · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Modelling brain tumours with organoids: towards precision medicine in neuro-oncology.Nature reviews. Neurology · 2026Review
- Article
- Molecular subtypes in pancreatic cancer: from academic promise to clinical reality.Molecular cancer · 2026Review
- moiraine: an R package to construct reproducible pipelines for the application and comparison of multi-omics integration methods.Bioinformatics (Oxford, England) · 2026Article
- Multiomic integration reveals tumoral heterogeneity of lipid dependence within lethal group 3 medulloblastoma.Cancer cell · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
54 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook-multi-omics mix (momix)-to foster reproducibility, and support users and future developers.
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