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missMethyl

Analysing Illumina HumanMethylation BeadChip Data

Bioconductor version: Release (3.17)

Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. 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. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.

Author: Belinda Phipson and Jovana Maksimovic

Maintainer: Belinda Phipson <phipson.b at wehi.edu.au>, Jovana Maksimovic <jovana.maksimovic at petermac.org>, Andrew Lonsdale <andrew.lonsdale at petermac.org>

Citation (from within R, enter citation("missMethyl")):

Installation

To install this package, start R (version "4.3") and enter: 

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("missMethyl")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("missMethyl")
missMethyl: Analysing Illumina HumanMethylation BeadChip Data HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews DNAMethylation, DifferentialMethylation, GeneSetEnrichment, GeneticVariability, GenomicVariation, MethylationArray, Normalization, Software
Version 1.34.0
In Bioconductor since BioC 3.0 (R-3.1) (9 years)
License GPL-2
Depends R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19
Imports AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment
Linking To
Suggests BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub
System Requirements
Enhances
URL
See More
Depends On Me methylationArrayAnalysis
Imports Me ChAMP, DMRcate, MEAL, methylGSA
Suggests Me RnBeads
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package missMethyl_1.34.0.tar.gz
Windows Binary missMethyl_1.34.0.zip (64-bit only)
macOS Binary (x86_64) missMethyl_1.34.0.tgz
macOS Binary (arm64) missMethyl_1.34.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/missMethyl
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/missMethyl
Bioc Package Browser https://code.bioconductor.org/browse/missMethyl/
Package Short Url https://bioconductor.org/packages/missMethyl/
Package Downloads Report Download Stats