R/justRMA.R

In HenrikBengtsson/aroma.affymetrix: Analysis of Large Affymetrix Microarray Data Sets

###########################################################################/**
# @RdocGeneric justRMA
# @alias justRMA.AffymetrixCelSet
# @alias justRMA.default
#
# @title "Robust Multichip Analysis (RMA) reproducing the affy package"
#
# \description{
#  @get "title" as far as possible.  The RMA method is described in [1].
#
#  The algorithm is processed in bounded memory, meaning a very large
#  number of arrays can be analyzed on also very limited computer systems.
#  The only limitation is the amount of memory required to load the final
#  chip-effect estimates into memory (as a @see "Biobase::ExpressionSet").
# }
#
# \usage{
#   @usage justRMA,AffymetrixCelSet
#   @usage justRMA,default
# }
#
# \arguments{
#  \item{csR}{An @see "AffymetrixCelSet".}
#  \item{flavor}{A @character string specifying the estimators used in
#   the RMA summarization step.}
#  \item{...}{Additional arguments passed to @see "doRMA" used internally.}
#  \item{verbose}{See @see "Verbose".}
# }
#
# \value{
#   Returns an annotated @see "Biobase::ExpressionSet".
# }
#
# \section{Reproducibility of affy}{
#   This implementation of the RMA method reproduces @see "affy::justRMA"
#   in \pkg{affy} package quite well.  It does so by still using a
#   constant memory profile, i.e. it is possible to use this implementation
#   to run RMA on a much large data set than what is possible with
#   \pkg{affy}.  At least 20-50 \emph{times} more samples should be
#   doable, if not more.
# }
#
# \references{
#  [1] Irizarry et al.
#      \emph{Summaries of Affymetrix GeneChip probe level data}.
#      NAR, 2003, 31, e15.\cr
# }
#
# \seealso{
#   @see "doRMA".
# }
#
# @author "HB"
#*/###########################################################################
setMethodS3("justRMA", "AffymetrixCelSet", function(csR, flavor=c("oligo", "affyPLM"), ..., verbose=FALSE) {
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # Validate arguments
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # Argument 'csR':
  className <- "AffymetrixCelSet"
  if (!inherits(csR, className)) {
    throw(sprintf("Argument 'csR' is not a %s: %s", className, class(csR)[1]))
  }

  # Argument 'flavor':
  flavor <- match.arg(flavor)

  # Argument 'verbose':
  verbose <- Arguments$getVerbose(verbose)

  verbose && enter(verbose, "justRMA() via aroma.affymetrix")

  # Run RMA
  ces <- doRMA(csR, drop=TRUE, flavor=flavor, ..., verbose=verbose)

  # Extract estimates
  eset <- extractExpressionSet(ces, orderUnitsBy="lexicographic",
                               annotationPkg="ChipDb", verbose=verbose)

  # BACKWARD COMPATIBIILTY/AD HOC:
  # Coerce std errors to missing values in case they are all 1.
  se.exprs <- .assayData(eset)$se.exprs
  if (all(se.exprs == 1)) {
    is.na(se.exprs) <- TRUE
    .assayData(eset)$se.exprs <- se.exprs
  }
  se.exprs <- NULL

  # Set the protocol data; scan dates
  data <- data.frame(ScanDate=getTimestamps(csR))
  rownames(data) <- gsub(",chipEffects", "", getFullNames(ces), fixed=TRUE)
  data <- as(data, "AnnotatedDataFrame")
  .protocolData(eset) <- data
  data <- NULL

  verbose && print(verbose, eset)
  verbose && exit(verbose)

  eset
}, protected=TRUE) # justRMA()


setMethodS3("justRMA", "default", function(...) {
  affy::justRMA(...)
}, protected=TRUE) # justRMA()