hicrep

DOI: 10.18129/B9.bioc.hicrep    

Measuring the reproducibility of Hi-C data

Bioconductor version: Release (3.6)

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance-dependence. We present a novel reproducibility measure that systematically takes these features into consideration. This measure can assess pairwise differences between Hi-C matrices under a wide range of settings, and can be used to determine optimal sequencing depth. Compared to existing approaches, it consistently shows higher accuracy in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages than existing approaches. This R package `hicrep` implements our approach.

Author: Tao Yang [aut, cre]

Maintainer: Tao Yang <xadmyangt at gmail.com>

Citation (from within R, enter citation("hicrep")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("hicrep")

Documentation

HTML R Script Evaluate reproducibility of Hi-C data with `hicrep`
PDF   Reference Manual
Text   NEWS

Details

biocViews HiC, QualityControl, Sequencing, Software
Version 1.2.0
In Bioconductor since BioC 3.5 (R-3.4) (1 year)
License GPL (>= 2.0)
Depends R (>= 3.4)
Imports stats
LinkingTo
Suggests knitr, rmarkdown, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package hicrep_1.2.0.tar.gz
Windows Binary hicrep_1.2.0.zip
Mac OS X 10.11 (El Capitan) hicrep_1.2.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/hicrep
Package Short Url http://bioconductor.org/packages/hicrep/
Package Downloads Report Download Stats

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