DNAm age estimation using different DNA methylation clocks.

Usage

DNAmAge(
  x,
  clocks = "all",
  toBetas = FALSE,
  fastImp = FALSE,
  normalize = FALSE,
  age,
  cell.count = TRUE,
  cell.count.reference = "blood gse35069 complete",
  min.perc = 0.8,
  ...
)

Arguments

x: data.frame (Individual in columns, CpGs in rows, CpG names in first colum - i.e. Horvath's format), matrix (individuals in columns and Cpgs in rows having CpG names in the rownames), ExpressionSet or GenomicRatioSet.
clocks: the methods used for estimating DNAmAge. Currrently "Horvath", "Hannum", "Levine", "BNN", "skinHorvath", "PedBE", "Wu", "TL", "BLUP", "EN", "NEOaPMA450K", "NEOaPNA450K", "NEOaPMAEPIC", "NEOaPNAEPIC" and "all" are available. Default is "all" and all clocks are estimated.
toBetas: Should data be transformed to beta values? Default is FALSE. If TRUE, it implies data are M values.
fastImp: Is fast imputation performed if necessary? (see details). Default is FALSE
normalize: Is Horvath's normalization performed? By default is FALSE
age: individual's chronological age.
cell.count: Are cell counts estimated? Default is TRUE.
cell.count.reference: Used when 'cell.count' is TRUE. Default is "blood gse35069 complete". See 'meffil::meffil.list.cell.count.references()' for possible values.
min.perc: Indicates the minimum conicidence percentage required between CpGs in or dataframee x and CpGs in clock coefficients to perform the calculation. If min.prec is too low, the estimated gestational DNAm age can be poor
...: Other arguments to be passed through impute package

Value

The estimated chronological and biological mDNA age

Details

Imputation is performed when having missing data. Fast imputation is performed by ... what about imputing only when CpGs for the clock are missing?

Examples

MethylationData <- read_csv(file.path(path, "MethylationDataExample55.csv"))
#> Error in read_csv(file.path(path, "MethylationDataExample55.csv")): could not find function "read_csv"
age.example55 <- DNAmAge(MethylationData)
#> Error in DNAmAge(MethylationData): object 'MethylationData' not found