ArticleNucleic acids research2022
Single-cell gene regulation network inference by large-scale data integration.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- Review
- Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.Nucleic acids research · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Quantifying Landscape-Flux via Single-Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3.Cell discovery · 2024Article
- Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.International journal of molecular sciences · 2024Review
- Integrative genomic analyses reveal putative cell type-specific targets of the Drosophila ets transcription factor Pointed.BMC genomics · 2024Article
- scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse.Nucleic acids research · 2024Article
- scKINETICS: inference of regulatory velocity with single-cell transcriptomics data.Bioinformatics (Oxford, England) · 2023Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Single-cell ATAC-seq (scATAC-seq) has proven to be a state-of-art approach to investigating gene regulation at the single-cell level. However, existing methods cannot precisely uncover cell-type-specific binding of transcription regulators (TRs) and construct gene regulation networks (GRNs) in single-cell. ChIP-seq has been widely used to profile TR binding sites in the past decades. Here, we developed SCRIP, an integrative method to infer single-cell TR activity and targets based on the integration of scATAC-seq and a large-scale TR ChIP-seq reference. Our method showed improved performance in evaluating TR binding activity compared to the existing motif-based methods and reached a higher consistency with matched TR expressions. Besides, our method enables identifying TR target genes as well as building GRNs at the single-cell resolution based on a regulatory potential model. We demonstrate SCRIP's utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems. SCRIP is freely available at https://github.com/wanglabtongji/SCRIP.
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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.