R/qn.bmiq.R

Defines functions qn.bmiq

Documented in qn.bmiq

#' Preprocess DNA Methylation Data
#'
#' This function runs a QN.BMIQ preprocessing pipeline on raw .idat files.
#'
#' @param targets A data frame of sample information or a vector of barcodes.
#' @param idats Path to the directory containing the relevant .idat files.
#'
#' @details
#' \code{qn.bmiq} provides wrappers for functions from several packages that
#' collectively form a complete preprocessing pipeline on raw DNA methylation 
#' data. It begins by filtering autosomal probes by detection \emph{p}-value, 
#' beadcount, alignment redundancy, and known SNPs using the default settings of
#' \code{ChAMP::\link[ChAMP]{champ.load}}. It then runs color bias adjustment, 
#' background correction, and quantile normalization, using functions from the 
#' \code{\link[lumi]{lumi}} package. Finally, data are normalized using 
#' beta-mixture quantile dilation as implemented by \code{
#' wateRmelon::\link[wateRmelon]{BMIQ}}. Missing values are imputed using 
#' \emph{k}-nearest neighbors with the default setting of \emph{k} = 10. Zeros 
#' are replaced with a very small value to facilitate logit transformation.
#'
#' Normalization procedures for methylation data are the subject of much active
#' research. Over a dozen methods are implemented in various Bioconductor 
#' packages. The QN.BMIQ pipeline has been found to be optimal in terms of both 
#' technical reproducibility (Marabita et al., 2013) and biological comparison 
#' with gold standard whole genome bisulfite sequencing (Wang et al., 2015).
#'
#' @return A matrix of filtered and normalized beta values.
#'
#' @references
#' Bolstad, B.M., Irizarry, R.A. Astrand, M. & Speed, T.P. (2003). 
#' \href{https://www.ncbi.nlm.nih.gov/pubmed/12538238}{A comparison of 
#' normalization methods for high density oligonucleotide array data based on
#' variance and bias}. \emph{Bioinformatics}, \emph{19}(2): 185-193.
#'
#' Marabita, F. et al. (2013). 
#' \href{https://www.ncbi.nlm.nih.gov/pubmed/23422812}{An evaluation of analysis 
#' pipelines for DNA methylation profiling using the Illumina 
#' HumanMethylation450 BeadChip platform}. \emph{Epigenetics}, \emph{8}(3): 
#' 333-346.
#' 
#' Morris, T.J., Butcher, L.M., Teschendorff, A.E., Chakravarthy, A.R., Wojdacz, 
#' T.K. & Beck, S. (2014). \href{http://doi.org/10.1093/bioinformatics/btt684}{
#' ChAMP: 450k Chip Analysis Methylation Pipeline}. \emph{Bioinformatics}, 
#' \emph{30}(3): 428-430.
#'
#' Teschendorff, A.E., Marabita, F., Lechner, M. Bartlett, T. Tegner, J. 
#' Gomez-Cabrero, D. & Beck, S. (2012). 
#' \href{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546795/}{A beta-mixture 
#' quantile normalization method for correcting probe design bias in Illumina 
#' Infinium 450k DNA methylation data}. \emph{Bioinformatics}, \emph{29}(2): 
#' 189-196.
#'
#' Wang, T. et al. (2015). 
#' \href{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623491/}{A systematic 
#' study of normalization methods for Infinium 450k methylation data using 
#' whole-genome bisulfite sequencing data}. \emph{Epigenetics}, \emph{10}(7): 
#' 662-669.
#'
#' @examples
#' 
#'
#' @export
#'

qn.bmiq <- function(targets, 
                    idats) {
  
  # Preliminaries
  if (!is.data.frame(targets)) {
    stop('targets must be a data frame containing sample information.')
  }
  if (!all(file.exists(idats))) {
    stop('At least one file in idats was not found. Check the paths.')
  }

  # Load libraries
  suppressPackageStartupMessages(require(ChAMP)) 
  suppressPackageStartupMessages(require(lumi))
  suppressPackageStartupMessages(require(wateRmelon))
  suppressPackageStartupMessages(require(ChAMPdata))
  suppressPackageStartupMessages(require(impute))
  
  # Filter
  dat <- champ.load(idats)
  keep <- rownames(dat$beta)
  dat <- importMethyIDAT(targets, idats)
  dat <- dat[keep, , drop = FALSE]

  # QN
  dat <- lumiMethyC(dat, method = 'quantile')    # Adjust for color bias
  dat <- lumiMethyB(dat, method = 'bgAdjust2C')  # Background correct
  dat <- lumiMethyN(dat, method = 'quantile')    # Quantile normalize
  betas <- 2L^(exprs(dat)) / (1L + 2L^(exprs(dat)))
  rm(dat)

  # BMIQ
  data(probeInfoALL.lv)                          # Annotate probes
  probeInfo.lv <- lapply(probeInfoALL.lv, function(x) {
    x[match(rownames(betas), probeInfoALL.lv[[5L]])]
  })
  design.v <- as.numeric(probeInfo.lv[[2L]])
  mat <- matrix(nrow = nrow(betas), ncol = ncol(betas), 
                dimnames = dimnames(betas))
  for (j in seq_len(ncol(betas))) {              # BMIQ normalize
    bmiq.o <- BMIQ(betas[, j], design.v, plots = FALSE, 
                   sampleID = colnames(betas)[j])
    mat[, j] <- bmiq.o$nbeta
  }
  mat <- impute.knn(mat)$data                    # Impute missing values
  mat[mat == 0L] <- 1e-7                         # Replace zeros

  # Export
  return(mat)

}
dswatson/biowrapr documentation built on July 6, 2018, 4:11 p.m.