ArticlePLoS computational biology2019
Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Single‑cell atlas reveals a Wilms' tumor 1‑mediated axis driving granulosa cell senescence in human ovarian aging.International journal of molecular medicine · 2026Article
- Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- Genome- and peak-informed two-stage framework for scATAC-seq cell type identification.Bioinformatics (Oxford, England) · 2026Article
- Developing a comprehensive database and search tool for single-cell ATAC-seq data.Scientific reports · 2026Article
- Exploratory multi-omics analysis suggests context-dependent immune responses to vitamin DFrontiers in immunology · 2026Article
- MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference.Frontiers in bioinformatics · 2026Article
- Simultaneously infer cell pseudotime, velocity field, and gene interaction from multi-branch scRNA-seq data with scPN.NAR genomics and bioinformatics · 2025Article
- Single Nucleus MultiOmics Links Novel Transcription Factor Motifs to Murine Hepatic Sex Differences in Chromatin Accessibility and Metabolic Dysfunction-Associated Steatotic Liver Disease.bioRxiv : the preprint server for biology · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.Nature biotechnology · 2025Article
- Integration of single cell multiomics data by deep transfer hypergraph neural network.Briefings in functional genomics · 2025Article
- Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology.Frontiers in genome editing · 2025Review
- Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Structure-guided integrative soft deep clustering analysis of scRNA-seq and scATAC-seq data.Frontiers in microbiology · 2025Article
- Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes.Genome research · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.Genome biology · 2024Article
- Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation.Life medicine · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
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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.