missMethyl: Analysing Illumina HumanMethylation BeadChip Data
Version 1.10.0

Normalisation and testing for differential variability and differential methylation 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. 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.

Browse man pages Browse package API and functions Browse package files

AuthorBelinda Phipson and Jovana Maksimovic
Bioconductor views DNAMethylation DifferentialMethylation GeneSetEnrichment GeneticVariability GenomicVariation MethylationArray Normalization
Date of publicationNone
MaintainerBelinda Phipson <belinda.phipson@mcri.edu.au>, Jovana Maksimovic <jovana.maksimovic@mcri.edu.au>
LicenseGPL-2
Version1.10.0
Package repositoryView on Bioconductor
InstallationInstall the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("missMethyl")

Man pages

contrasts.varFit: Compute contrasts for a varFit object.
densityByProbeType: Plot the beta value distributions of the Infinium I and II...
getINCs: Extract intensity data for 613 Illumina negative controls...
getLeveneResiduals: Obtain Levene residuals
getMappedEntrezIDs: Get mapped Entrez Gene IDs from CpG probe names
gometh: Gene ontology testing for Ilumina methylation array data
gsameth: Generalised gene set testing for Illumina's methylation array...
missMethyl-package: Introduction to the missMethyl package
RUVadj: Adjust estimated variances
RUVfit: Remove unwanted variation when testing for differential...
SWAN: Subset-quantile Within Array Normalisation for Illumina...
topGSA: Get table of top 20 enriched pathways
topRUV: Table of top-ranked differentially methylated CpGs obatained...
topVar: Table of top-ranked differentially variable CpGs
varFit: Testing for differential variability

Functions

RUVadj Man page Source code
RUVfit Man page Source code
SWAN Man page Source code
SWAN.MethyLumiSet Man page Source code
SWAN.RGChannelSet Man page Source code
SWAN.default Man page Source code
aveQuantile Source code
contrasts.varFit Man page Source code
densityByProbeType Man page Source code
estimatePWF Source code
flattenAnn Source code
getINCs Man page Source code
getLeveneResiduals Man page Source code
getMappedEntrezIDs Man page Source code
getSubset Source code
gometh Man page Source code
gsameth Man page Source code
missMethyl Man page
missMethyl-package Man page
normalizeTypes Source code
plotBias Source code
subsetQuantileNorm Source code
toList Source code
toMArrayLM Source code
topGSA Man page Source code
topRUV Man page Source code
topVar Man page Source code
varFit Man page Source code
varFit.DGEList Man page Source code
varFit.MethylSet Man page Source code
varFit.default Man page Source code

Files

DESCRIPTION
NAMESPACE
R
R/DiffVar.R
R/RUVfunctions.R
R/SWAN.R
R/gometh.R
R/gsameth.R
R/plottingFunctions.R
build
build/vignette.rds
inst
inst/CITATION
inst/NEWS.Rd
inst/doc
inst/doc/missMethyl.R
inst/doc/missMethyl.Rmd
inst/doc/missMethyl.html
man
man/RUVadj.Rd
man/RUVfit.Rd
man/SWAN.Rd
man/contrasts.varFit.Rd
man/densityByProbeType.Rd
man/getINCs.Rd
man/getLeveneResiduals.Rd
man/getMappedEntrezIDs.Rd
man/gometh.Rd
man/gsameth.Rd
man/missMethyl-package.Rd
man/topGSA.Rd
man/topRUV.Rd
man/topVar.Rd
man/varFit.Rd
vignettes
vignettes/bibliography.bib
vignettes/missMethyl.Rmd
missMethyl documentation built on May 20, 2017, 9:44 p.m.