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: | 1.0.8 |
| Depends: | R (≥ 2.10), DiffBind, Rsamtools, DESeq2, lattice, stats, utils, graphics |
| Published: | 2018-02-15 |
| 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 |
| Reference manual: | Brundle.pdf |
| Package source: | Brundle_1.0.8.tar.gz |
| Windows binaries: | r-devel: Brundle_1.0.8.zip, r-release: Brundle_1.0.8.zip, r-oldrel: Brundle_1.0.8.zip |
| OS X El Capitan binaries: | r-release: not available |
| OS X Mavericks binaries: | r-oldrel: Brundle_1.0.7.tgz |
| Old sources: | Brundle archive |
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