Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
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 )
``` |

`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' |

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

`da, anno` |
annotation data frame, such as [email protected]@data #methylumi package. If NULL, the swan method requires the |

`qc` |
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). |

**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

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

[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

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wateRmelon documentation built on Nov. 1, 2018, 2:26 a.m.

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