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:
Applications Notes
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
Descripción
Sumario: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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