To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("charm")
In most cases, you don't need to download the package archive at all.
charm
Analysis of DNA methylation data from CHARM microarrays
Bioconductor version: 3.0
This package implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools.
Author: Martin Aryee, Peter Murakami, Harris Jaffee, Rafael Irizarry
Maintainer: Peter Murakami <pmurakam at jhu.edu>
Citation (from within R,
enter citation("charm")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("charm")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("charm")
| R Script | charm Vignette | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DNAMethylation, Microarray, Software |
| Version | 2.12.0 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | LGPL (>= 2) |
| Depends | R (>= 2.14.0), Biobase, SQN, fields, RColorBrewer, genefilter |
| Imports | BSgenome, Biobase, oligo(>= 1.11.31), oligoClasses(>= 1.17.39), ff, preprocessCore, methods, stats, Biostrings, IRanges, siggenes, nor1mix, gtools, grDevices, graphics, utils, limma, parallel, sva(>= 3.1.2) |
| LinkingTo | |
| Suggests | charmData, BSgenome.Hsapiens.UCSC.hg18, corpcor |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | charmData |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | charm_2.12.0.tar.gz |
| Windows Binary | charm_2.12.0.zip |
| Mac OS X 10.6 (Snow Leopard) | charm_2.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | charm_2.12.0.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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