ArticleNature genetics2024
Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Article
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- Human body single-cell atlas of three-dimensional genome organization and DNA methylation.Science (New York, N.Y.) · 2026Article
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Annotation-Based Gene-Peak Links Improve Regulatory Network Prediction of Gene Expression in Human Kidney Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Echo characterizes the desynchronization of gene expression and chromatin accessibility during cell-state transitions.bioRxiv : the preprint server for biology · 2026Article
- Article
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- Article
- Article
- SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiation.bioRxiv : the preprint server for biology · 2026Article
- Dissecting PGENature communications · 2026Article
- Boolean logic links chromatin accessibility states to gene expression variability across cell types.Nucleic acids research · 2026Article
- FOXA1 mutations co-opt nascent transcription factor networks in partnership with androgen receptor to enhance prostate tumorigenicity.Cell reports · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Why "Where" Matters as Much as "How Much": Single-Cell and Spatial Transcriptomics in Plants.International journal of molecular sciences · 2025Review
- SCRIPT: Predicting Single-Cell Long-Range Cis-Regulation Based on Pretrained Graph Attention Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.
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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.