ReviewNature reviews. Genetics2019
Integrative single-cell analysis.
Review in Nature reviews. Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 849 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
849 citing papers in PubMed.
- Immunophenotyping by Single-Cell CITE-Seq.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Single-cell transcriptomics identifies PRDX1 and GPX4 as potential ferroptosis regulators and tumor microenvironment--related molecules in hepatocellular carcinoma.Oncology letters · 2026Article
- High-resolution spatial transcriptomics of irradiated submandibular glands treated with fortified fibrin hydrogels.iScience · 2026Article
- Article
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Spatial Proteomics as a Potential Decision-Support Layer for Early Melanoma: A Narrative Review.International journal of dermatology · 2026Review
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- HPRT1 drives mitochondrial metabolic reprogramming and immunosuppressive microenvironment in oral squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2026Article
- A spatiotemporal single-cell transcriptomic atlas reveals fertilization-induced auxin signaling driving strawberry fruit set and development.Nature communications · 2026Article
- Epigenomic Remodeling by Lipid Metabolism: A Cross-Disease Perspective on Cancer and Brain Disorders.Genes · 2026Review
- Review
- Special Issue "Machine Learning and Bioinformatics in Human Health and Disease 2nd Edition".International journal of molecular sciences · 2026Article
- scFlowReport: A Reproducible Workflow for Comparative Downstream Biological Analysis of Single-Cell RNA-seq Data.Biomolecules · 2026Article
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.Neural regeneration research · 2026Article
- The neural stem cell‑NSCLC axis: Molecular drivers, microenvironment crosstalk and emerging therapies (Review).International journal of oncology · 2026Review
- Integrative single-cell and machine learning analysis identifies a tumor doubling time-related prognostic signature and therapeutic targets in head and neck squamous cell carcinoma.Translational oncology · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.Journal of the National Cancer Institute · 2026Article
789 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.
Indexed as
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30696980What OpenQuestion holds
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.