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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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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. |
format | Online Article Text |
id | pubmed-5860379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT liutong schicnormasoftwarepackagetoeliminatesystematicbiasesinsinglecellhicdata AT wangzheng schicnormasoftwarepackagetoeliminatesystematicbiasesinsinglecellhicdata |