adjustedFunnorm {wateRmelon} | R Documentation |
adjustedFunnorm utilizes functional normliasation to normalise autosomal CpGs, and infers the sex chromosome linked CpGs by linear interpolation on corrected autosomal CpGs.
adjustedFunnorm( rgSet, nPCs = 2, sex = NULL, bgCorr = TRUE, dyeCorr = TRUE, keepCN = TRUE, ratioConvert = TRUE, verbose = TRUE )
rgSet |
An object of class "RGChannelSet". |
nPCs |
Number of principal components from the control probes PCA. |
sex |
An optional numeric vector containing the sex of the samples. |
bgCorr |
Should the NOOB background correction be done, prior to functional normalization (see "preprocessNoob") |
dyeCorr |
Should dye normalization be done as part of the NOOB background correction (see "preprocessNoob")? |
keepCN |
Should copy number estimates be kept around? Setting to 'FALSE' will decrease the size of the output object significantly. |
ratioConvert |
Should we run "ratioConvert", ie. should the output be a "GenomicRatioSet" or should it be kept as a "GenomicMethylSet"; the latter is for experts. |
verbose |
Should the function be verbose? |
an object of class "GenomicRatioSet", unless "ratioConvert=FALSE" in which case an object of class "GenomicMethylSet".
Functional normalization of 450k methylation array data improves replication
in large cancer studies, Fortin et al., 2014, Genome biology.
interpolatedXY: a two-step strategy to normalise DNA methylation
microarray data avoiding sex bias, Wang et al., 2021.
## Not run: GRset <- adjustedFunnorm(RGSet) ## End(Not run)