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")
):
To install this package, start R and enter:
## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("hicrep")
HTML | R Script | Evaluate reproducibility of Hi-C data with `hicrep` |
Reference Manual | ||
Text | NEWS |
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 | |
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Build Report |
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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