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normr

Normalization and difference calling in ChIP-seq data


Bioconductor version: Release (3.18)

Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.

Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut]

Maintainer: Johannes Helmuth <johannes.helmuth at laborberlin.com>

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

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("normr")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("normr")
Introduction to the normR package HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Alignment, Bayesian, ChIPSeq, Classification, DataImport, DifferentialPeakCalling, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, RIPSeq, Software
Version 1.28.0
In Bioconductor since BioC 3.4 (R-3.3) (7.5 years)
License GPL-2
Depends R (>= 3.3.0)
Imports methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue(>= 2.2), bamsignals(>= 1.4), rtracklayer(>= 1.32)
System Requirements C++11
URL https://github.com/your-highness/normR
Bug Reports https://github.com/your-highness/normR/issues
See More
Suggests BiocStyle, testthat (>= 1.0), knitr, rmarkdown
Linking To Rcpp
Enhances BiocParallel
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report Build Report

Package Archives

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

Source Package normr_1.28.0.tar.gz
Windows Binary normr_1.28.0.zip (64-bit only)
macOS Binary (x86_64) normr_1.28.0.tgz
macOS Binary (arm64) normr_1.28.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/normr
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/normr
Bioc Package Browser https://code.bioconductor.org/browse/normr/
Package Short Url https://bioconductor.org/packages/normr/
Package Downloads Report Download Stats