This is the released version of multiHiCcompare; for the devel version, see multiHiCcompare.
Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are availableBioconductor version: Release (3.21)
multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
Author: Mikhail Dozmorov [aut, cre] ORCID: 0000-0002-0086-8358 , John Stansfield [aut]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov at gmail.com>
Citation (from within R, entercitation("multiHiCcompare")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("multiHiCcompare")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("multiHiCcompare")Follow Installation instructions to use this package in your R session.
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