ArticleBioinformatics (Oxford, England)2020
GiniQC: a measure for quantifying noise in single-cell Hi-C data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Harmonizing single-cell 3D genome data with STARK and scNucleome.Genome biology · 2026Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- HiC4D-SPOT: a spatiotemporal outlier detection tool for Hi-C data.Briefings in bioinformatics · 2025Article
- Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deciphering single-cell genomic architecture: insights into cellular heterogeneity and regulatory dynamics.Genomics & informatics · 2025Review
- [Advances in methods and applications of single-cell Hi-C data analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Single-cell Hi-C data analysis: safety in numbers.Briefings in bioinformatics · 2021Article
- Resources and challenges for integrative analysis of nuclear architecture data.Current opinion in genetics & development · 2021Review
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Authors and funding
3 authors.
Funding
Abstract
summarySingle-cell Hi-C (scHi-C) allows the study of cell-to-cell variability in chromatin structure and dynamics. However, the high level of noise inherent in current scHi-C protocols necessitates careful assessment of data quality before biological conclusions can be drawn. Here, we present GiniQC, which quantifies unevenness in the distribution of inter-chromosomal reads in the scHi-C contact matrix to measure the level of noise. Our examples show the utility of GiniQC in assessing the quality of scHi-C data as a complement to existing quality control measures. We also demonstrate how GiniQC can help inform the impact of various data processing steps on data quality. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available at https://github.com/4dn-dcic/GiniQC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.