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


ABSTRACT: 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. Contact:zheng.wang@miami.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Liu T 

PROVIDER: S-EPMC5860379 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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

Liu Tong T   Wang Zheng Z  

Bioinformatics (Oxford, England) 20180301 6


<h4>Summary</h4>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.<h4>Availability and implementation</h4>scHiCNorm is available at http://dna.cs.miami.edu/scHiCNorm/. Perl scripts are provided that can generate bias features. Pre-built b  ...[more]

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