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biocLite("missMethyl")
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missMethyl
Analysis of methylation array data
Bioconductor version: 3.0
Normalisation and testing for differential variability for data from Illumina's Infinium HumanMethylation450 array. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test.
Author: Belinda Phipson and Jovana Maksimovic
Maintainer: Belinda Phipson <belinda.phipson at mcri.edu.au>, Jovana Maksimovic <jovana.maksimovic at mcri.edu.au>
Citation (from within R,
enter citation("missMethyl")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("missMethyl")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("missMethyl")
| R Script | missMethyl: analysing data from Illumina's HumanMethylation450 array | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DNAMethylation, DifferentialMethylation, GeneticVariability, GenomicVariation, MethylationArray, Normalization, Software |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL-2 |
| Depends | R (>= 2.3.0) |
| Imports | limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod |
| LinkingTo | |
| Suggests | minfiData, BiocStyle, edgeR, tweeDEseqCountData |
| 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 | missMethyl_1.0.0.tar.gz |
| Windows Binary | missMethyl_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | missMethyl_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | missMethyl_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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