Evidence map›Paper›PMID 37847655›Full record

ArticleBioinformatics (Oxford, England)2023

scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data.

Huiling Liu, Wenxiu Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Differential analysis of image-based chromatin tracing data with Dory.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
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

2 authors.

Huiling LiuDepartment of Statistics, University of California Riverside, Riverside, CA 92521, United States.
Wenxiu MaDepartment of Statistics, University of California Riverside, Riverside, CA 92521, United States.ORCID 0000-0003-4097-1621

Funding

Computational modeling of spatial genome organization and gene regulationR35GM133678 · NIGMS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI MA, WENXIU · 2019 to 2023
$1.9M
Acquisition of a Scalable Storage Cluster for Data Intensive NIH ResearchS10OD016290 · OD · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI GIRKE, THOMAS · 2014 to 2014
$593k
NIGMS NIH HHS R35 GM133678NIH HHS 1S10OD016290
6 · The paper itself

Abstract

summaryHere, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level. AVAILABILITY AND IMPLEMENTATION: scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).

Indexed as

Carcinoma, Intraductal, NoninfiltratingChromatinChromosomesHumansModels, StatisticalSoftwareChromatin

Identifiers

PMID37847655
PMCPMC10598576

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.