ArticleNature computational science2023
Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.
Erratum issuedOpen access · hybridAbstract read
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Article in Nature computational science, 2023. 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 19 papers.
0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 25 citations in OpenAlex.
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- Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging.Cell reports · 2026Article
- Integrative Multi-omics Analysis of the Human Skeletal Muscle Response to Endurance or Resistance Exercise: Findings from the Molecular Transducers of Physical Activity Consortium (MoTrPAC).bioRxiv : the preprint server for biology · 2026Article
- Exercise modulation of the alternative splicing landscape in human tissues.bioRxiv : the preprint server for biology · 2026Article
- BARTsc identifies key transcriptional regulators from single-cell omics data.bioRxiv : the preprint server for biology · 2026Article
- Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.Briefings in bioinformatics · 2025Article
- Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise.Genome research · 2025Article
- Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions.bioRxiv : the preprint server for biology · 2025Article
- A guide to studying 3D genome structure and dynamics in the kidney.Nature reviews. Nephrology · 2025Review
- Identification and validation of core biomarkers for sepsis: a comprehensive analysis using bioinformatics and machine learning.Frontiers in immunology · 2025Article
- Machine learning tools for deciphering the regulatory logic of enhancers in health and disease.Frontiers in genetics · 2025Review
- scGO: interpretable deep neural network for cell status annotation and disease diagnosis.Briefings in bioinformatics · 2024Article
- Automated single-cell omics end-to-end framework with data-driven batch inference.Cell systems · 2024Article
- Automated single-cell omics end-to-end framework with data-driven batch inference.bioRxiv : the preprint server for biology · 2024Article
- Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation.Life medicine · 2024Review
- Integrated epigenomic exposure signature discovery.Epigenomics · 2024Article
- Altered epigenetic landscape as infectious disease diagnostics.Epigenomics · 2024Article
- Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.Nature computational science · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
28 authors at 8 institutions in 1 country.
Xi ChenCenter for Computational Biology, Flatiron Institute, New York, NY, USA.
Yuan WangDepartment of Computer Science, Princeton University, Princeton, NJ, USA.ORCID 0000-0001-7750-3587
Antonio CappuccioDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Wan-Sze ChengDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Frederique Ruf ZamojskiDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-2451-8598
Venugopalan D NairDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Clare M MillerDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Aliza B RubensteinDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
German NudelmanDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Alicja TadychLewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
Chandra L TheesfeldLewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.ORCID 0000-0002-8379-6600
Alexandria VornholtDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Mary-Catherine GeorgeDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Felicia RuffinDivision of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Michael DagherDivision of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Daniel G ChawlaProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
Alessandra Soares-SchanoskiDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Rachel R SpurbeckBattelle Memorial Institute, Columbus, OH, USA.
Lishomwa C NdhlovuDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Robert SebraDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Steven H KleinsteinProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.ORCID 0000-0003-4957-1544
Andrew G LetiziaNaval Medical Research Center, Silver Spring, MD, USA.ORCID 0000-0002-7361-5917
Irene RamosDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-0223-0120
Vance G FowlerDivision of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Christopher W WoodsDivision of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Elena ZaslavskyDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-4828-7771
Olga G TroyanskayaCenter for Computational Biology, Flatiron Institute, New York, NY, USA.
Stuart C SealfonDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0001-5791-1217
Icahn School of Medicine at Mount Sinai · USDuke University · USPrinceton University · USNaval Sea Systems Command · USYale University · USBattelle · USCornell University · USNaval Medical Research Command · US
Funding
HOPE - HIV Obstruction by Programmed EpigeneticsUM1AI164559 · NIAID · J. DAVID GLADSTONE INSTITUTES · PI Lishomwa C Ndhlovu, Melanie Maria Ott · 2021 to 2026
$32.2MKPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4MElucidating single cell changes in neurogenic brain regions during HIV and cannabinoid exposureU01DA058527 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Michael Jay Corley, Lishomwa C Ndhlovu · 2023 to 2026
$9.4Mlntegration and Visualization of Diverse Biological DataR01GM071966 · NIGMS · PRINCETON UNIVERSITY · PI TROYANSKAYA, OLGA G · 2005 to 2022
$6.4MIntegrative Single Cell isoform and chromatin accessibility Mapping of Chronic Opioid Exposure in Cognitive Brain Areas in HIVU01DA053625 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI MILNER, TERESA A, NDHLOVU, LISHOMWA C · 2021 to 2025
$4.0MHLA Fine Mapping to Elucidate S. aureus SusceptibilityR01AI165671 · NIAID · DUKE UNIVERSITY · PI FOWLER, VANCE G., SCOTT, WILLIAM K · 2021 to 2025
$3.8MDevelopment of Brain Organoids to Study the Impact of HIV-1, Drugs of Abuseand Aging on Cognitive ImpairmentR01DA052027 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI NIXON, DOUGLAS F · 2020 to 2024
$3.7MNIAID NIH HHS R01 AI165671NIAID NIH HHS UM1 AI164559NIDA NIH HHS R01 DA052027NIDA NIH HHS U01 DA053625NIDA NIH HHS U01 DA058527NIDDK NIH HHS U01 DK133090NIGMS NIH HHS R01 GM071966
Abstract
Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study
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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.