ArticleGenome biology2019
Single-cell transcriptomics unveils gene regulatory network plasticity.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 1 of them a synthesis that pooled it.
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Who cites it
120 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities.Briefings in bioinformatics · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- Integration of single-cell regulon atlas and bulk RNA-seq for individualized prognostic prediction in stomach adenocarcinoma.iScience · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Embryonic Hormetic Priming Modulates Later-Life Thermal Tolerance.Ecology and evolution · 2026Article
- Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep.Biology · 2026Article
- Microbial Omics.Progress in molecular and subcellular biology · 2026Review
- GP-DHT: A dual-head transformer with supervised contrastive learning for shared-gene pretraining and single-cell GRN prediction.PloS one · 2026Article
- Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- A cell type and state specific gene regulation network inference method for immune regulatory analysis.NPJ systems biology and applications · 2025Article
- Emerging Concepts in Pathogenesis, Multiomics Applications, and Clinical Research in Lymphangioleiomyomatosis.Seminars in respiratory and critical care medicine · 2025Review
- Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.Briefings in bioinformatics · 2025Article
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- Identification of shared important genes associated with ferroptosis across different etiologies of acute lung injury.Scientific reports · 2025Article
- Selective phase separation of transcription factors is driven by orthogonal molecular grammar.Nature communications · 2025Article
- Single-cell network biology enabling cell-type-resolved disease genetics.Genomics & informatics · 2025Review
60 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.
resultsWe devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.
conclusionsOur approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.
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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.