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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DMRcate")
In most cases, you don't need to download the package archive at all.
DMRcate
Illumina 450K methylation array spatial analysis methods
Bioconductor version: 3.0
De novo identification and extraction of differentially methylated regions (DMRs) in the human genome using Illumina Infinium HumanMethylation450 BeadChip array data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes bedGraph generation, GRanges generation and plotting functions.
Author: Tim Peters
Maintainer: Tim Peters <Tim.Peters at csiro.au>
Citation (from within R,
enter citation("DMRcate")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DMRcate")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DMRcate")
| R Script | The DMRcate package user's guide | |
| Reference Manual | ||
| Text | NEWS | |
| Text | LICENSE |
Details
| biocViews | DNAMethylation, DifferentialExpression, DifferentialMethylation, GeneExpression, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, OneChannel, QualityControl, Software, TimeCourse, TwoChannel |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | file LICENSE |
| Depends | R (>= 3.1.0), limma, minfi, DMRcatedata |
| Imports | methods, graphics |
| LinkingTo | |
| Suggests | knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomicRanges |
| SystemRequirements | |
| Enhances | |
| 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 | DMRcate_1.2.0.tar.gz |
| Windows Binary | DMRcate_1.2.0.zip |
| Mac OS X 10.6 (Snow Leopard) | DMRcate_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | DMRcate_1.2.0.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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