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.
|Author||Belinda Phipson and Jovana Maksimovic|
|Date of publication||None|
|Maintainer||Belinda Phipson <firstname.lastname@example.org>, Jovana Maksimovic <email@example.com>|
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