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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("chipenrich")
In most cases, you don't need to download the package archive at all.
chipenrich
Gene set enrichment for ChIP-seq peak data
Bioconductor version: 3.0
ChIP-Enrich performs gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes.
Author: Ryan P. Welch [aut, cre, cph], Chee Lee [aut, cre], Raymond G. Cavalcante [aut, cre], Laura J. Scott [ths], Maureen A. Sartor [ths]
Maintainer: Ryan P. Welch <welchr at umich.edu>, Chee Lee <cheelee at umich.edu>, Raymond G. Cavalcante <rcavalca at umich.edu>
Citation (from within R,
enter citation("chipenrich")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("chipenrich")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("chipenrich")
| R Script | ChIP-Enrich Vignette/Tutorial | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Bioinformatics, Enrichment, GeneSetEnrichment, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-3 |
| Depends | R (>= 2.15.1) |
| Imports | chipenrich.data, methods, GenomicRanges(>= 1.10.0), IRanges(>= 1.16.0), mgcv, plyr (>= 1.7.0), lattice, latticeExtra, grid, stringr (>= 0.6), rms |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | parallel |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | chipenrich_1.4.0.tar.gz |
| Windows Binary | chipenrich_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | chipenrich_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | chipenrich_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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