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missMethyl (version 1.6.2)

Analysis of methylation array data

Description

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.

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Version

Version

1.6.2

License

GPL-2

Maintainer

Belinda Phipson

Last Published

February 15th, 2017

Functions in missMethyl (1.6.2)

gsameth

Generalised gene set testing for 450K methylation data
missMethyl-package

Introduction to the missMethyl package
getMappedEntrezIDs

Get mapped Entrez Gene IDs from CpG probe names
getINCs

Extract intensity data for 613 Illumina negative controls found on 450k arrays.
topGSA

Get table of top 20 enriched pathways
topRUV

Table of top-ranked differentially methylated CpGs obatained from a differential methylation analysis using RUV
gometh

Gene ontology testing for 450K methylation data
contrasts.varFit

Compute contrasts for a varFit object.
SWAN

Subset-quantile Within Array Normalisation for Illumina Infinium HumanMethylation450 BeadChips
topVar

Table of top-ranked differentially variable CpGs
RUVadj

Adjust estimated variances
RUVfit

Remove unwanted variation when testing for differential methylation
densityByProbeType

Plot the beta value distributions of the Infinium I and II probe types relative to the overall beta value distribution.
varFit

Testing for differential variability
getLeveneResiduals

Obtain Levene residuals