Evidence map›Paper›PMID 38589516›Full record

ArticleNature methods2024

scGHOST: identifying single-cell 3D genome subcompartments.

Kyle Xiong, Ruochi Zhang, Jian Ma

Erratum issuedAbstract read
In one paragraph

Article in Nature methods, 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 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Article
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  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  14. Article
  15. Article
  16. Article
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  18. Review
  19. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Kyle Xiong *Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Ruochi Zhang *Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-1103-5044
Jian MaRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. jianma@cs.cmu.edu.ORCID http://orcid.org/0000-0002-4202-5834

Funding

Multiscale Analyses of 4D Nucleome Structure and Function by Comprehensive Multimodal Data IntegrationUM1HG011593 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI ALBER, FRANK, BELMONT, ANDREW STEVEN · 2020 to 2024
$10.4M
Computational Methods for Next-Generation Comparative GenomicsR01HG007352 · NHGRI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MA, JIAN · 2014 to 2023
$2.8M
Computational methods for studying single-cell 3D genomeR01HG012303 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI DUAN, ZHIJUN, MA, JIAN · 2022 to 2025
$2.2M
Spatial omics technologies to map the senescent cell microenvironmentUH3CA268202 · NCI · BROWN UNIVERSITY · PI MA, JIAN, NERETTI, NICOLA · 2023 to 2025
$2.2M
Spatial omics technologies to map the senescent cell microenvironmentUG3CA268202 · NCI · BROWN UNIVERSITY · PI MA, JIAN, NERETTI, NICOLA · 2021 to 2022
$916k
Single-cell multiomic methods for studying genome structure and functionR56HG013092 · NHGRI · UNIVERSITY OF WASHINGTON · PI DUAN, ZHIJUN, MA, JIAN · 2023 to 2023
$400k
NCI NIH HHS UG3 CA268202NCI NIH HHS UH3 CA268202NHGRI NIH HHS R01 HG007352NHGRI NIH HHS R01 HG012303NHGRI NIH HHS R56 HG013092NHGRI NIH HHS UM1 HG011593U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HG012303U.S. Department of Health & Human Services | National Institutes of Health (NIH) UM1HG011593
6 · The paper itself

Abstract

Single-cell Hi-C (scHi-C) technologies allow for probing of genome-wide cell-to-cell variability in three-dimensional (3D) genome organization from individual cells. Computational methods have been developed to reveal single-cell 3D genome features based on scHi-C, including A/B compartments, topologically associating domains and chromatin loops. However, no method exists for annotating single-cell subcompartments, which is important for understanding chromosome spatial localization in single cells. Here we present scGHOST, a single-cell subcompartment annotation method using graph embedding with constrained random walk sampling. Applications of scGHOST to scHi-C data and contact maps derived from single-cell 3D genome imaging demonstrate reliable identification of single-cell subcompartments, offering insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from complex tissues, scGHOST identifies cell-type-specific or allele-specific subcompartments linked to gene transcription across various cell types and developmental stages, suggesting functional implications of single-cell subcompartments. scGHOST is an effective method for annotating single-cell 3D genome subcompartments in a broad range of biological contexts.

Indexed as

Single-Cell AnalysisAnimalsChromatinGenomeHumansImaging, Three-DimensionalMiceChromatin

Identifiers

PMID38589516
PMCPMC11127718

What OpenQuestion holds

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Registered trials

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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.