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###########################################################################/**
# @set "class=array"
# @RdocMethod calmateByTotalAndFracB
# @alias calmateByTotalAndFracB
#
# @title "Normalize allele-specific copy numbers (total,fracB)"
#
# \description{
# @get "title", where total is the total (non-polymorphic) signal and
# fracB is the allele B fraction.
# It is only loci with a non-missing (@NA) fracB value that are
# considered to be SNPs and normalized by CalMaTe. The other loci
# are left untouched.
# }
#
# @synopsis
#
# \arguments{
# \item{data}{An Jx2xI @numeric @array, where J is the number of loci,
# 2 is total and fracB (in that order, if unnamed), and
# I is the number of samples.}
# \item{references}{A @logical or @numeric @vector specifying which
# samples should be used as the reference set.
# By default, all samples are considered. If not NULL at least 3 samples.}
# \item{...}{Additional arguments passed to @seemethod "calmateByThetaAB".}
# \item{refAvgFcn}{(optional) A @function that takes a JxI @numeric @matrix
# an argument \code{na.rm} and returns a @numeric @vector of length J.
# It should calculate some type of average for each of the J rows, e.g.
# @see "matrixStats::rowMedians".
# If specified, then the total copy numbers of the calibrated ASCNs
# are standardized toward (twice) the average of the total copy numbers
# of the calibrated reference ASCNs.}
# \item{verbose}{See @see "R.utils::Verbose".}
# }
#
# \value{
# Returns an Jx2xI @numeric @array
# with the same dimension names as argument \code{data}.
# }
#
# @examples "../incl/calmateByTotalAndFracB.Rex"
#
# \references{
# [1] @include "../incl/OrtizM_etal_2012.Rd" \cr
# }
#
# \seealso{
# To calibrate (thetaA,thetaB) or (CA,CB) signals,
# see @seemethod "calmateByThetaAB".
# }
#*/###########################################################################
setMethodS3("calmateByTotalAndFracB", "array", function(data, references=NULL, ..., refAvgFcn=NULL, verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'data':
if (!is.array(data)) {
throw("Argument 'data' is not an array: ", class(data)[1]);
}
dim <- dim(data);
dimnames <- dimnames(data);
if (length(dim) != 3) {
throw("Argument 'data' is not a 3-dimensional array: ",
paste(dim, collapse="x"));
}
if (dim[2] != 2) {
throw("Argument 'data' is not a Jx2xI-dimensional array: ",
paste(dim, collapse="x"));
}
if (!is.null(dimnames[[2]])) {
# Backward compatibility
if (dimnames(data)[[2]][2] == "freqB"){
dimnames(data)[[2]][2] <- "fracB";
}
if (!identical(dimnames(data)[[2]], c("total", "fracB"))) {
throw("If given, the names of the allele (2nd) dimension of the Jx2xI-dimensional array (argument 'data') have to be 'total' & 'fracB': ", paste(dimnames(data)[[2]], collapse=", "));
}
}
# From here on we force dimension names on the 2nd dimension
dimnames(data)[[2]] <- c("total", "fracB");
nbrOfSamples <- dim[3];
if (nbrOfSamples < 3) {
throw("Argument 'data' contains less than three samples: ", nbrOfSamples);
}
if (nbrOfSamples < 3) {
throw("Argument 'data' contains less than three samples: ", nbrOfSamples);
}
# Argument 'references':
if (is.null(references)) {
# The default is that all samples are used to calculate the reference.
references <- seq_len(nbrOfSamples);
} else if (is.logical(references)) {
if (length(references) != nbrOfSamples) {
throw("Length of argument 'references' does not match the number of samples in argument 'data': ", length(references), " != ", nbrOfSamples);
}
references <- which(references);
} else if (is.numeric(references)) {
references <- as.integer(references);
if (any(references < 1 | references > nbrOfSamples)) {
throw(sprintf("Argument 'references' is out of range [1,%d]: %d", nbrOfSamples, length(references)));
}
}
if (length(references) < 3) {
throw("Argument 'reference' specify less than three reference samples: ", length(references));
}
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose);
verbose && enter(verbose, "calmateByTotalAndFracB()");
verbose && cat(verbose, "(total,fracB) signals:");
verbose && str(verbose, data);
verbose && enter(verbose, "Identifying SNPs (non-missing fracB)");
nok <- is.na(data[,"fracB",,drop=FALSE]);
dim(nok) <- dim(nok)[-2]; # Drop the 2nd dimension
nok <- rowAlls(nok);
## save(nok, file="nok.Rdata");
snps <- which(!nok);
verbose && printf(verbose, "Number of SNPs: %d (%.2f%%)\n",
length(snps), 100*length(snps)/dim(data)[1]);
verbose && exit(verbose);
verbose && enter(verbose, "Transforming SNPs to (thetaA, thetaB)");
theta <- data[snps,,,drop=FALSE];
theta <- totalAndFracB2ThetaAB(theta, verbose=less(verbose, 5));
verbose && str(verbose, theta);
verbose && exit(verbose);
thetaC <- calmateByThetaAB(theta, references=references, ..., verbose=verbose);
theta <- NULL ## Not needed anymore
verbose && enter(verbose, "Backtransforming SNPs to (total, fracB)");
dataC <- data;
dataC[snps,,] <- thetaAB2TotalAndFracB(thetaC, verbose=less(verbose, 5));
verbose && str(verbose, dataC);
snps <- thetaC <- NULL ## Not needed anymore
verbose && exit(verbose);
verbose && enter(verbose, "Calibrating non-polymorphic probes");
# Extract total CNs
units <- which(nok);
nok <- NULL ## Not needed anymore
theta <- data[units,"total",,drop=FALSE];
dim(theta) <- dim(theta)[-2]; # Drop the 2nd dimension
thetaC <- fitCalMaTeCNprobes(theta, references=references);
theta <- NULL ## Not needed anymore
dataC[units,"total",] <- thetaC;
## aux <- dataC[units,,,drop=FALSE];
## save(aux,file="dataC.Rdata")
verbose && str(verbose, dataC[units,,,drop=FALSE]);
units <- thetaC <- NULL ## Not needed anymore
verbose && exit(verbose);
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Standardize toward a custom average of the references?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (!is.null(refAvgFcn)) {
verbose && enter(verbose, "Standardize total copy numbers toward the average reference signals");
# Extract reference total copy number signals
yCR <- dataC[,"total",references,drop=FALSE];
dim(yCR) <- dim(yCR)[-2]; # Drop 2nd dimension
# Calculate the average
yCR <- refAvgFcn(yCR, na.rm=TRUE);
# Standardize total copy numbers to this average
dataC[,"total",] <- (2/yCR) * dataC[,"total",,drop=FALSE];
yCR <- NULL ## Not needed anymore
verbose && exit(verbose);
}
# Enforce the same dimension names as the input data
dimnames(dataC) <- dimnames;
verbose && cat(verbose, "Calibrated (total,fracB) signals:");
verbose && str(verbose, dataC);
verbose && exit(verbose);
dataC;
}) # calmateByTotalAndFracB()
###########################################################################
# HISTORY:
# 2011-12-15 [HB]
# o CLEANUP: Tidied up the validation of argument 'references' and
# improved the corresponding error messages.
# 2011-12-07 [MO]
# o Number of references has to be at least 3.
# 2011-03-18 [HB]
# o BUG FIX: calmateByTotalAndFracB() required that the 2nd dimension
# of argument 'data' had names "total" and "fracB".
# 2010-08-05 [HB]
# o ROBUSTNESS: Now calmateByTotalAndFracB() asserts that there is at
# least two samples.
# o BUG FIX: calmateByTotalAndFracB() assumed that there where enough
# units and samples so that subsetting would not drop singleton
# dimension. Now we use drop=FALSE everywhere.
# 2010-08-02 [HB]
# o Added argument 'refAvgFcn' to calmateByTotalAndFracB().
# o CLEANUP: Removed save() calls used for debugging.
# 2010-06-22 [MO]
# o Now calmateByTotalAndFracB() calibrates also non-polymorphic loci.
# 2010-06-18 [HB]
# o Now calmateByTotalAndFracB() handles also non-polymorphic loci.
# 2010-06-04 [MO]
# o Created.
###########################################################################
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