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scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data

SUMMARY: We build a software package scHiCNorm that uses zero-inflated and hurdle models to remove biases from single-cell Hi-C data. Our evaluations prove that our models can effectively eliminate systematic biases for single-cell Hi-C data, which better reveal cell-to-cell variances in terms of ch...

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Detalles Bibliográficos
Autores principales: Liu, Tong, Wang, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860379/
https://www.ncbi.nlm.nih.gov/pubmed/29186290
http://dx.doi.org/10.1093/bioinformatics/btx747
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author Liu, Tong
Wang, Zheng
author_facet Liu, Tong
Wang, Zheng
author_sort Liu, Tong
collection PubMed
description SUMMARY: We build a software package scHiCNorm that uses zero-inflated and hurdle models to remove biases from single-cell Hi-C data. Our evaluations prove that our models can effectively eliminate systematic biases for single-cell Hi-C data, which better reveal cell-to-cell variances in terms of chromosomal structures. AVAILABILITY AND IMPLEMENTATION: scHiCNorm is available at http://dna.cs.miami.edu/scHiCNorm/. Perl scripts are provided that can generate bias features. Pre-built bias features for human (hg19 and hg38) and mouse (mm9 and mm10) are available to download. R scripts can be downloaded to remove biases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-58603792018-03-21 scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data Liu, Tong Wang, Zheng Bioinformatics Applications Notes SUMMARY: We build a software package scHiCNorm that uses zero-inflated and hurdle models to remove biases from single-cell Hi-C data. Our evaluations prove that our models can effectively eliminate systematic biases for single-cell Hi-C data, which better reveal cell-to-cell variances in terms of chromosomal structures. AVAILABILITY AND IMPLEMENTATION: scHiCNorm is available at http://dna.cs.miami.edu/scHiCNorm/. Perl scripts are provided that can generate bias features. Pre-built bias features for human (hg19 and hg38) and mouse (mm9 and mm10) are available to download. R scripts can be downloaded to remove biases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-03-15 2017-11-23 /pmc/articles/PMC5860379/ /pubmed/29186290 http://dx.doi.org/10.1093/bioinformatics/btx747 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Liu, Tong
Wang, Zheng
scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title_full scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title_fullStr scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title_full_unstemmed scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title_short scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data
title_sort schicnorm: a software package to eliminate systematic biases in single-cell hi-c data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860379/
https://www.ncbi.nlm.nih.gov/pubmed/29186290
http://dx.doi.org/10.1093/bioinformatics/btx747
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AT wangzheng schicnormasoftwarepackagetoeliminatesystematicbiasesinsinglecellhicdata