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>

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