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source("http://bioconductor.org/biocLite.R")
biocLite("MethylMix")
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MethylMix
MethylMix: Identifying methylation driven cancer genes.
Bioconductor version: 3.0
MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression.
Author: Olivier Gevaert
Maintainer: Olivier Gevaert <olivier.gevaert at gmail.com>
Citation (from within R,
enter citation("MethylMix")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("MethylMix")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MethylMix")
| R Script | MethylMix | |
| Reference Manual |
Details
| biocViews | DNAMethylation, DifferentialExpression, DifferentialMethylation, GeneExpression, GeneRegulation, MethylationArray, Network, Pathways, Software, StatisticalMethod |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL-2 |
| Depends | R (>= 3.1.1) |
| Imports | foreach, parallel, doParallel, RColorBrewer, optimx, RPMM |
| LinkingTo | |
| Suggests | BiocStyle |
| 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 | MethylMix_1.0.0.tar.gz |
| Windows Binary | MethylMix_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | MethylMix_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | MethylMix_1.0.0.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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