Nothing
computeOutlier <- function(afbatch,rm.mask = TRUE, rm.outliers = TRUE, rm.extra = TRUE,
celfile.path = NULL,celfile.names = NULL) {
#######################################################################
#
# Arguments:
# afbatch - an AffyBatch object containing the names of the chips
# to calculate outliers
# rm.outliers,
# rm.mask,
# rm.extra - logical value to remove outliers and/or masked
# values. If rm.extra = TRUE, it overrides the values
# of rm.outliers and rm.mask. Works identically to
# these parameters in ReadAffy(), which it calls
# celfile.path - character string giving the directory path to
# the *.CEL files being read, or NULL if the *.CEL
# files are in the current directory
#
# Value:
# a matrix containing the list of outliers / masked values for
# the given AffyBatch object. The number of rows in the matrix
# is equal to the number of probes for a .CEL file, and the
# number of columns is equal to the number of chips (columns of
# AffyBatch). The value of each location in the matrix will be
# TRUE if the corresponding probe is an outlier / masked value
# and FALSE if it is not. The probes will be arranged in the
# same order as the intensity values, so that the outliers
# belonging to a specific probeset can be accessed using the
# pmindex / mmindex functions. Note that this function assumes
# the .CEL files are still available in the current directory.
#
# Data dictionary:
# filenames - a list of the filenames corresponding to the
# AffyBatch object, in the same order as the
# AffyBatch columns, for reading in the .CEL file
# data
# cel - an AffyBatch object containing the .CEL file data with
# the outliers flagged
#######################################################################
# get the filenames corresponding to the AffyBatch object
if (is.null(celfile.names))
filenames <- sampleNames(afbatch) else
filenames <- celfile.names
writeLines("Computing outliers")
# read in the .CEL files again, omitting the outliers. This has to be
# done because the ReadAffy() function returns NAs for the outliers, so
# we cannot access the values of outliers in later calculations
# otherwise
cel <- ReadAffy(filenames=filenames,rm.mask=rm.mask,rm.outliers=rm.outliers,rm.extra=rm.extra,celfile.path=celfile.path)
# set up the outlier matrix with TRUE/FALSE values
outlier <- is.na(intensity(cel))
return(outlier)
}
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