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

SUMMARY: Here, 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 demonstrate...

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Detalles Bibliográficos
Autores principales: Liu, Huiling, Ma, Wenxiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598576/
https://www.ncbi.nlm.nih.gov/pubmed/37847655
http://dx.doi.org/10.1093/bioinformatics/btad625
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author Liu, Huiling
Ma, Wenxiu
author_facet Liu, Huiling
Ma, Wenxiu
author_sort Liu, Huiling
collection PubMed
description SUMMARY: Here, 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).
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spelling pubmed-105985762023-10-26 scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data Liu, Huiling Ma, Wenxiu Bioinformatics Applications Note SUMMARY: Here, 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). Oxford University Press 2023-10-17 /pmc/articles/PMC10598576/ /pubmed/37847655 http://dx.doi.org/10.1093/bioinformatics/btad625 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Liu, Huiling
Ma, Wenxiu
scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title_full scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title_fullStr scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title_full_unstemmed scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title_short scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data
title_sort schicdiff: detecting differential chromatin interactions in single-cell hi-c data
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598576/
https://www.ncbi.nlm.nih.gov/pubmed/37847655
http://dx.doi.org/10.1093/bioinformatics/btad625
work_keys_str_mv AT liuhuiling schicdiffdetectingdifferentialchromatininteractionsinsinglecellhicdata
AT mawenxiu schicdiffdetectingdifferentialchromatininteractionsinsinglecellhicdata

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