Brundle: Normalisation Tools for Inter-Condition Variability of ChIP-Seq Data

Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.

Version:	1.0.9
Depends:	R (≥ 2.10), DiffBind, Rsamtools, DESeq2, lattice, stats, utils, graphics
Published:	2019-04-23
Author:	Andrew N Holding
Maintainer:	Andrew N Holding <andrew.holding at cruk.cam.ac.uk>
License:	CC BY 4.0
NeedsCompilation:	no
Materials:	README
CRAN checks:	Brundle results

Documentation:

Reference manual:

Brundle.pdf

Downloads:

Package source:	Brundle_1.0.9.tar.gz
Windows binaries:	r-devel: Brundle_1.0.9.zip, r-release: Brundle_1.0.9.zip, r-oldrel: Brundle_1.0.9.zip
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): Brundle_1.0.9.tgz, r-release (x86_64): Brundle_1.0.9.tgz, r-oldrel (x86_64): Brundle_1.0.9.tgz
Old sources:	Brundle archive

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