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
[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