Evidence map›Paper›PMID 32946435›Full record

ArticlePLoS computational biology2020

Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data.

Hyeon-Jin Kim, Galip Gürkan Yardımcı, Giancarlo Bonora, Vijay Ramani, Jie Liu, Ruolan Qiu, Choli Lee, Jennifer Hesson, Carol B Ware, Jay Shendure and 2 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing 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

74 citing papers in PubMed.

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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hyeon-Jin KimDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-6938-4602
Galip Gürkan YardımcıDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-4858-9681
Giancarlo BonoraDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-7558-3798
Vijay RamaniDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Jie LiuDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-9504-0587
Ruolan QiuDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Choli LeeDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-8901-1835
Jennifer HessonDepartment of Comparative Medicine, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-5685-6279
Carol B WareDepartment of Comparative Medicine, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-7171-6685
Jay ShendureDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Zhijun DuanDivision of Hematology, Department of Medicine, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-8147-793X
William Stafford NobleDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-7283-4715

Funding

University of Washington Center for Nuclear Organization and FunctionU54DK107979 · NIDDK · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD, SHENDURE, JAY ASHOK · 2015 to 2019
$11.8M
NIDDK NIH HHS U54 DK107979
6 · The paper itself

Abstract

Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.

Indexed as

ChromatinCell LineCluster AnalysisComputational BiologyGene LibraryHigh-Throughput Nucleotide SequencingHumansNatural Language ProcessingSingle-Cell AnalysisChromatin

Identifiers

PMID32946435
PMCPMC7526900

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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.