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

Tong Liu, Zheng Wang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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 informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1046-1047
Number of pages2
JournalBioinformatics
Volume34
Issue number6
DOIs
StatePublished - Mar 15 2018

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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