ArticleScience advances2020
Predicting transcription factor binding in single cells through deep learning.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- How negative sampling shapes the performance of transcription factor binding site prediction models.Bioinformatics (Oxford, England) · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer.Briefings in bioinformatics · 2025Article
- Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis.Developmental cell · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease.PLoS computational biology · 2024Article
- Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds.Tree physiology · 2024Article
- REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- MMGAT: a graph attention network framework for ATAC-seq motifs finding.BMC bioinformatics · 2024Article
- GNNMF: a multi-view graph neural network for ATAC-seq motif finding.BMC genomics · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.Nucleic acids research · 2023Article
- InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop.Briefings in bioinformatics · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Cellcano: supervised cell type identification for single cell ATAC-seq data.Nature communications · 2023Article
- A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.International journal of molecular sciences · 2023Article
- Applications of deep learning in understanding gene regulation.Cell reports methods · 2023Review
- GenomicKB: a knowledge graph for the human genome.Nucleic acids research · 2023Article
- maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.PLoS computational biology · 2023Article
- Single-cell gene regulation network inference by large-scale data integration.Nucleic acids research · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Characterizing genome-wide binding profiles of transcription factors (TFs) is essential for understanding biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining them at a single-cell level remains elusive. Here, we report scFAN (single-cell factor analysis network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pretrained on genome-wide bulk assay for transposase-accessible chromatin sequencing (ATAC-seq), DNA sequence, and chromatin immunoprecipitation sequencing (ChIP-seq) data and uses single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by both studying sequence motifs enriched within predicted binding peaks and using predicted TFs for discovering cell types. We develop a new metric "TF activity score" to characterize each cell and show that activity scores can reliably capture cell identities. scFAN allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.
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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.