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Brundle  

Normalisation Tools for Inter-Condition Variability of ChIP-Seq Data
View on CRAN: Click here


Download and install Brundle package within the R console
Install from CRAN:
install.packages("Brundle")

Install from Github:
library("remotes")
install_github("cran/Brundle")

Install by package version:
library("remotes")
install_version("Brundle", "1.0.9")



Attach the package and use:
library("Brundle")
Maintained by
Andrew N Holding
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-19
Latest Update: 2023-10-05
Description:
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) .
How to cite:
Andrew N Holding (2017). Brundle: Normalisation Tools for Inter-Condition Variability of ChIP-Seq Data. R package version 1.0.9, https://cran.r-project.org/web/packages/Brundle
Previous versions and publish date:
1.0.5 (2017-09-19 19:27), 1.0.7 (2017-12-10 20:52), 1.0.8 (2018-02-16 00:47), 1.0.9 (2019-04-24 00:00)
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