ReviewNature reviews. Nephrology2021
Multi-omics integration in the age of million single-cell data.
Review in Nature reviews. Nephrology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
133 citing papers in PubMed.
- Epigenetic regulation of kidney development.Nature reviews. Nephrology · 2026Review
- Bacterial adaptation to essential oils and their main compounds in food systems: mechanisms related to antibacterial resistance.NPJ science of food · 2026Review
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- A deep learning and large language hybrid workflow for omics interpretation.Nature biomedical engineering · 2026Article
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Conserved Transcriptional Circuits Regulate Cardiac Fibroblast-Mediated Fibrosis.Circulation research · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- Interplay between high-energy quenching and state transitions in Chlamydomonas reinhardtii: a single-cell approach.The New phytologist · 2026Article
- Decoding the CAF-TAM axis: multi-omics dissection and therapeutic targeting of stromal-immune crosstalk in the tumor microenvironment.Cell death & disease · 2026Review
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026Review
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Article
- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
73 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.
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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.