dasen: Calculate normalized betas from Illumina 450K methylation arrays

Description

Multiple ways of calculating the index of methylation (beta) from methylated and unmethylated probe intensities used in Pidsley et al 2012. S4 methods exist where possible for MethyLumiSet, MethylSet, RGSet and exprmethy450 objects.

Usage

dasen ( mns, uns, onetwo, fudge = 100, ret2=FALSE, ... )
nasen ( mns, uns, onetwo, ret2=FALSE, fudge = 100 )
betaqn( bn ) 
naten ( mn,  un, fudge = 100, ret2=FALSE )
naten ( mn,  un, fudge = 100, ret2=FALSE )
nanet ( mn,  un, fudge = 100, ret2=FALSE )
nanes ( mns, uns, onetwo, fudge = 100, ret2=FALSE, ... )
danes ( mn,  un,  onetwo, fudge = 100, ret2=FALSE, ... ) 
danet ( mn,  un,  onetwo, fudge = 100, ret2=FALSE, ... )
danen ( mns, uns, onetwo, fudge = 100, ret2=FALSE, ... )
daten1( mn,  un,  onetwo, fudge = 100, ret2=FALSE, ... )
daten2( mn,  un,  onetwo, fudge = 100, ret2=FALSE, ... )
tost  ( mn,  un,  da, pn )
fuks  ( data, anno)
swan  ( mn,  un,  qc, da=NULL, return.MethylSet=FALSE )

Arguments

mn, mns

matrix of methylated signal intensities, each column representing a sample (generic) or a MethyLumiSet, RGSet, or MethylSet object. Column names are used to get Sentrix row and column by default, see '...'.

un, uns

matrix of unmethylated signal intensities, each column representing a sample (default method) or NULL when mn is an object containing methylated and unmethylated values

bn, data

matrix of precalculated betas, each column representing a sample

onetwo

character vector or factor of length nrow(mn) indicating assay type 'I' or 'II'

matrix of detection p-values, each column representing a sample

da, anno

annotation data frame, such as x@featureData@data #methylumi package. If NULL, the swan method requires the IlluminaHumanMethylation450kmanifest package.

control probe intensities: list of 2 matrices, Cy3 and Cy5, with rownames, such as produced by intensitiesByChannel(QCdata(x)) #methylumi package

fudge

value added to total intensity to prevent denominators close to zero when calculating betas

return.MethylSet

if TRUE, returns a MethylSet object instead of a naked matrix of betas.

ret2

if TRUE, returns a list of intensities and betas instead of a naked matrix of betas.

...

additional argument roco for dfsfit giving Sentrix rows and columns. This allows a background gradient model to be fit. This is split from data column names by default. roco=NULL disables model fitting (and speeds up processing), otherwise roco can be supplied as a character vector of strings like 'R01C01' (only 3rd and 6th characters used).

Value

a matrix (default method) or object of the same shape and order as the first argument containing betas.

Details

dasen same as nasen but type I and type II backgrounds are equalized first. This is our recommended method

betaqn quantile normalizes betas

naten quantile normalizes methylated and unmethylated intensities separately, then calculates betas

nanet quantile normalizes methylated and unmethylated intensities together, then calculates betas. This should equalize dye bias

nanes quantile normalizes methylated and unmethylated intensities separately, except for type II probes where methylated and unmethylated are normalized together. This should equalize dye bias without affecting type I probes which are not susceptible

danes same as nanes, except type I and type II background are equalized first

danet same as nanet, except type I and type II background are equalized first

danen background equalization only, no normalization

daten1 same as naten, except type I and type II background are equalized first (smoothed only for methylated)

daten2 same as naten, except type I and type II background are equalized first (smoothed for methylated an unmethylated)

nasen same as naten but type I and typeII intensities quantile normalized separately

tost method from Touleimat and Tost 2011

fuks method from Dedeurwaerder et al 2011. Peak correction only, no normalization

swan method from Maksimovic et al 2012

References

[1] Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC: A data-driven approach to preprocessing Illumina 450K methylation array data (submitted)

[2] Dedeurwaerder S, Defrance M, Calonne E, Sotiriou C, Fuks F: Evaluation of the Infinium Methylation 450K technology . Epigenetics 2011, 3(6):771-784.

[3] Touleimat N, Tost J: Complete pipeline for Infinium R Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 2012, 4:325-341.

[4] Maksimovic J, Gordon L, Oshlack A: SWAN: Subset quantile Within-Array Normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome biology 2012, 13(6):R44

Examples

Run this code


#MethyLumiSet method
data(melon)
melon.dasen <- dasen(melon)

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