Multiple ways of calculating the index of methylation (beta) from methylated and unmethylated probe intensities used in Pidsley et al 2012.
A MethyLumiSet object. Sample names names are used to get Sentrix row and column by default, see '...'.
value added to total intensity to prevent denominators close to zero when calculating 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).
dasen same as nasen but type I and type II backgrounds are normalized 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 typeI and type II background are equalised first.
danet same as nanet, except typeI and type II background are equalised first.
danen background equalisation only, no normalization
daten1 same as naten, except typeI and type II background are equalised first (smoothed only for methylated)
daten2 same as naten, except typeI and type II background are equalised first (smoothed for methylated an unmethylated)
nasen same as naten but typeI 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
a matrix (default method) or object of the same shape and order as the first argument containing betas.
dasen ( mns, fudge = 100, ... ) nasen ( mns, fudge = 100 ) betaqn( bn ) naten ( mn, fudge = 100 ) naten ( mn, fudge = 100 ) nanet ( mn, fudge = 100 ) nanes ( mns,fudge = 100 ) danes ( mn, fudge = 100, ... ) danet ( mn, fudge = 100, ... ) danen ( mns,fudge = 100, ... ) daten1( mn, fudge = 100, ... ) daten2( mn, fudge = 100, ... ) tost ( mn ) fuks ( data) swan ( mn )
 Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC: A data-driven approach to preprocessing Illumina 450K methylation array data (submitted)
 Dedeurwaerder S, Defrance M, Calonne E, Sotiriou C, Fuks F: Evaluation of the Infinium Methylation 450K technology . Epigenetics 2011, 3(6):771-784.
 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
 Maksimovic J, Gordon L, Oshlack A: SWAN: Subset quantile Within-Array Normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome biology 2012, 13(6):R44
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.