R/AdditiveCovariatesNormalization.R
In HenrikBengtsson/aroma.affymetrix: Analysis of Large Affymetrix Microarray Data Sets
###########################################################################/**
# @RdocClass AdditiveCovariatesNormalization
#
# @title "The AdditiveCovariatesNormalization class"
#
# \description{
# @classhierarchy
#
# This class represents a normalization method that corrects for
# GC-content effects on copy-number chip-effect estimates.
# }
#
# @synopsis
#
# \arguments{
# \item{dataSet}{A @see "SnpChipEffectSet".}
# \item{...}{Additional arguments passed to the constructor of
# @see "ChipEffectTransform".}
# \item{target}{(Optional) A @character string or a @function
# specifying what to normalize toward.}
# \item{subsetToFit}{The units from which the normalization curve should
# be estimated. If @NULL, all are considered.}
# \item{onMissing}{Specifies how to normalize units for which the
# GC contents are unknown.}
# \item{shift}{An optional amount the data points should be shifted
# (translated).}
# }
#
# \section{Fields and Methods}{
# @allmethods "public"
# }
#
# \details{
# For SNPs, the normalization function is estimated based on the total
# chip effects, i.e. the sum of the allele signals. The normalizing
# is done by rescale the chip effects on the intensity scale such that
# the mean of the total chip effects are the same across samples for
# any given GC content. For allele-specific estimates, both alleles
# are always rescaled by the same amount. Thus, when normalizing
# allele-specific chip effects, the total chip effects is change, but not
# the relative allele signal, e.g. the allele B frequency.
# }
#
# @author
#*/###########################################################################
setConstructorS3("AdditiveCovariatesNormalization", function(dataSet=NULL, ..., target=NULL, subsetToFit="-XY", shift=0, onMissing=c("median", "ignore")) {
extraTags <- NULL
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'dataSet':
if (!is.null(dataSet)) {
dataSets <- Arguments$getInstanceOf(dataSet, "SnpChipEffectSet")
}
# Argument 'target':
if (!is.null(target)) {
if (is.function(target)) {
target <- list(target)
}
if (is.character(target)) {
if (target == "zero") {
} else {
throw("Unknown value of argument 'target': ", target)
}
} else if (is.list(target)) {
# Validate each element
for (kk in seq_along(target)) {
if (!is.function(target[[kk]])) {
throw("One element in 'target' is not a function: ",
class(target[[kk]])[1])
}
}
} else {
throw("Unknown value of argument 'target': ", class(target)[1])
}
}
# Argument 'subsetToFit':
if (is.null(subsetToFit)) {
} else if (is.character(subsetToFit)) {
if (subsetToFit %in% c("-X", "-Y", "-XY")) {
} else {
throw("Unknown value of argument 'subsetToFit': ", subsetToFit)
}
extraTags <- c(extraTags, subsetToFit=subsetToFit)
} else {
unf <- getUnitNamesFile(dataSet)
subsetToFit <- Arguments$getIndices(subsetToFit, max=nbrOfUnits(unf))
subsetToFit <- unique(subsetToFit)
subsetToFit <- sort(subsetToFit)
}
# Argument 'shift':
shift <- Arguments$getDouble(shift, disallow=c("NA", "NaN", "Inf"))
# Argument 'onMissing':
onMissing <- match.arg(onMissing)
extend(ChipEffectTransform(dataSet, ...), "AdditiveCovariatesNormalization",
.subsetToFit = subsetToFit,
"cached:.target" = target,
.onMissing = onMissing,
.extraTags = extraTags,
shift = shift
)
})
setMethodS3("getAsteriskTags", "AdditiveCovariatesNormalization", function(this, collapse=NULL, ...) {
tags <- NextMethod("getAsteriskTags", collapse=collapse)
# Extra tags?
tags <- c(tags, this$.extraTags)
# Add class-specific tags
shift <- as.integer(round(this$shift))
if (shift != 0) {
tags <- c(tags, sprintf("%+d", shift))
}
# Collapse?
tags <- paste(tags, collapse=collapse)
tags
}, protected=TRUE)
setMethodS3("getParameters", "AdditiveCovariatesNormalization", function(this, expand=TRUE, ...) {
# Get parameters from super class
params <- NextMethod("getParameters", expand=expand)
# Get parameters of this class
params <- c(params, list(
subsetToFit = this$.subsetToFit,
onMissing = this$.onMissing,
.target = this$.target,
shift = this$shift
))
# Expand?
if (expand) {
subsetToFit <- getSubsetToFit(this)
}
params
}, protected=TRUE)
setMethodS3("getCdf", "AdditiveCovariatesNormalization", function(this, ...) {
inputDataSet <- getInputDataSet(this)
getCdf(inputDataSet)
})
setMethodS3("getOutputDataSet00", "AdditiveCovariatesNormalization", function(this, ..., verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
verbose && enter(verbose, "Getting input data set")
ces <- getInputDataSet(this)
verbose && cat(verbose, "Class: ", class(ces)[1])
verbose && exit(verbose)
verbose && enter(verbose, "Getting output data set for ", class(this)[1])
args <- list(generic="getOutputDataSet", object=this, ...)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Inherit certain arguments from the input data set
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (inherits(ces, "CnChipEffectSet"))
args$combineAlleles <- ces$combineAlleles
if (inherits(ces, "SnpChipEffectSet"))
args$mergeStrands <- ces$mergeStrands
verbose && cat(verbose, "Calling NextMethod() with arguments:")
verbose && str(verbose, args)
args$verbose <- less(verbose, 10)
res <- do.call(NextMethod, args)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(res, "SnpChipEffectSet")) {
verbose && enter(verbose, "Carrying down parameters for ", class(res)[1])
res$mergeStrands <- ces$mergeStrands
if (inherits(res, "CnChipEffectSet")) {
res$combineAlleles <- ces$combineAlleles
}
verbose && exit(verbose)
}
# Let the set update itself
update2(res, verbose=less(verbose, 1))
verbose && exit(verbose)
res
}, protected=TRUE)
setMethodS3("getCovariates", "AdditiveCovariatesNormalization", abstract=TRUE, protected=TRUE)
setMethodS3("getSubsetToFit", "AdditiveCovariatesNormalization", function(this, force=FALSE, ..., verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
# Cached?
units <- this$.units
if (!is.null(units) && !force)
return(units)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Identifying all potential units, i.e. SNPs and CN probes
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
verbose && enter(verbose, "Identifying units that are SNP and CN probes")
# Get SNP information
cdf <- getCdf(this)
nbrOfUnits <- nbrOfUnits(cdf)
# Identify all SNP and CN units (==potential units to be fitted)
verbose && enter(verbose, "Identifying SNPs and CN probes")
types <- getUnitTypes(cdf, verbose=less(verbose,1))
verbose && print(verbose, table(types))
units <- which(types == 2 | types == 5)
# Not needed anymore
types <- NULL
verbose && cat(verbose, "units:")
verbose && str(verbose, units)
verbose && exit(verbose)
onMissing <- this$.onMissing
if (onMissing == "ignore") {
# Keep only those for which we have annotation data covariates on
# for at least one covariate
verbose && enter(verbose, "Reading annotation data covariates")
X <- getCovariates(this, units=units, verbose=less(verbose,5))
keep <- rep(FALSE, nrow(X))
for (ee in seq_len(ncol(X))) {
keep <- keep | is.finite(X[,ee])
}
units <- units[keep]
verbose && printf(verbose, "Number of SNP/CN units without finite covariates: %d out of %d (%.1f%%)\n", sum(!keep), length(keep), 100*sum(!keep)/length(keep))
verbose && exit(verbose)
# Not needed anymore
keep <- X <- NULL
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Subset with a prespecified set of units?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
subsetToFit <- this$.subsetToFit
if (is.character(subsetToFit)) {
if (subsetToFit %in% c("-X", "-Y", "-XY")) {
verbose && enter(verbose, "Identify subset of units from genome information")
verbose && cat(verbose, "subsetToFit: ", subsetToFit)
# Look up in cache
subset <- this$.subsetToFitExpanded
if (is.null(subset)) {
# Get the genome information (throws an exception if missing)
gi <- getGenomeInformation(cdf)
verbose && print(verbose, gi)
# Identify units to be excluded
if (subsetToFit == "-X") {
subset <- getUnitsOnChromosomes(gi, 23, .checkArgs=FALSE)
} else if (subsetToFit == "-Y") {
subset <- getUnitsOnChromosomes(gi, 24, .checkArgs=FALSE)
} else if (subsetToFit == "-XY") {
subset <- getUnitsOnChromosomes(gi, 23:24, .checkArgs=FALSE)
}
verbose && cat(verbose, "Units to exclude: ")
verbose && str(verbose, subset)
# The units to keep
subset <- setdiff(seq_len(nbrOfUnits), subset)
verbose && cat(verbose, "Units to include: ")
verbose && str(verbose, subset)
# Store
this$.subsetToFitExpanded <- subset
}
subsetToFit <- subset
# Not needed anymore
subset <- NULL
verbose && exit(verbose)
}
}
if (!is.null(subsetToFit)) {
# A fraction subset?
if (length(subsetToFit) == 1 && 0 < subsetToFit && subsetToFit < 1) {
keep <- seq(from=1, to=length(units), length=subsetToFit*length(units))
} else {
keep <- which(units %in% subsetToFit)
}
verbose && enter(verbose, "Reading annotation data covariates")
X <- getCovariates(this, units=units, verbose=less(verbose,5))
verbose && str(verbose, X)
verbose && exit(verbose)
# Make sure to keep data points at the tails too
extremeUnits <- c()
for (ee in seq_len(ncol(X))) {
extremeUnits <- c(extremeUnits, which.min(X[,ee]), which.max(X[,ee]))
}
# Not needed anymore
X <- NULL
keep <- c(keep, extremeUnits)
keep <- unique(keep)
# Now filter
units <- units[keep]
# Not needed anymore
keep <- NULL
}
# Sort units
units <- sort(units)
# Assert correctness
units <- Arguments$getIndices(units, max=nbrOfUnits)
# Cache
this$.units <- units
verbose && exit(verbose)
units
}, private=TRUE)
setMethodS3("getTargetFunctions", "AdditiveCovariatesNormalization", function(this, ..., force=FALSE, verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
target <- this$.target
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Handling argument 'force'
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# AD HOC: If forced, make sure to use the original target argument
if (force) {
if (!is.null(target)) {
targetType <- attr(target, "targetType")
if (!is.null(targetType))
target <- targetType
# Not needed anymore
targetType <- NULL
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Predefined target functions?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (is.character(target)) {
verbose && enter(verbose, "Setting up predefined target functions")
targetType <- target
verbose && cat(verbose, "Target type: ", targetType)
# Infer the number of covariates
X <- getCovariates(this, units=1:5)
nbrOfCovariates <- ncol(X)
# Not needed anymore
X <- NULL
if (identical(targetType, "zero")) {
target <- rep(list(function(...) log2(2200)), nbrOfCovariates)
} else {
throw("Unknown target function: ", targetType)
}
# Store the original target type in an attribute
attr(target, "targetType") <- targetType
this$.target <- target
force <- FALSE
verbose && exit(verbose)
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Target functions based on the average array?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (force || is.null(target)) {
# Get target set
ces <- getInputDataSet(this)
verbose && enter(verbose, "Getting average signal across arrays")
ceR <- getAverageFile(ces, force=force, verbose=less(verbose))
# Not needed anymore
ces <- NULL; # Not needed anymore
verbose && exit(verbose)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
# Get units to fit
units <- getSubsetToFit(this)
verbose && cat(verbose, "Units:")
verbose && str(verbose, units)
# Get target signals for SNPs
yR <- extractTheta(ceR, units=units, verbose=less(verbose, 5))
# Not needed anymore
ceR <- NULL; # Not needed anymore
verbose && cat(verbose, "(Allele-specific) thetas:")
verbose && str(verbose, yR)
# If more than one theta per unit, sum them up to get the total signal
if (ncol(yR) > 1) {
# Row sums with na.rm=TRUE => NAs are treated as zeros.
yR[is.na(yR)] <- 0
for (cc in 2:ncol(yR)) {
yR[,1] <- yR[,1] + yR[,cc]
}
}
yR <- yR[,1,drop=TRUE]
verbose && cat(verbose, "Total thetas:")
verbose && str(verbose, yR)
verbose && cat(verbose, "Summary of total signals (on the intensity scale):")
verbose && summary(verbose, yR)
# Shift?
shift <- this$shift
if (shift != 0) {
verbose && printf(verbose, "Applying shift: %+f\n", shift)
yR <- yR + shift
verbose && cat(verbose, "Summary of shifted signals (on the intensity scale):")
verbose && summary(verbose, yR)
}
yR <- log2(yR)
verbose && cat(verbose, "Signals:")
verbose && str(verbose, yR)
verbose && cat(verbose, "Summary of signals (on the log2 scale):")
verbose && summary(verbose, yR)
# Get PCR fragment lengths for these
X <- getCovariates(this, units=units, verbose=less(verbose,5))
# Not needed anymore
units <- NULL; # Not needed anymore
verbose && cat(verbose, "Annotation data covariates:")
verbose && str(verbose, X)
verbose && cat(verbose, "Summary of covariates:")
verbose && summary(verbose, X)
# Fit lowess function
verbose && enter(verbose, "Fitting target prediction function to each covariate exclusively")
okYR <- is.finite(yR)
verbose && cat(verbose, "Distribution of log2 signals that are finite:")
verbose && summary(verbose, okYR)
hasX <- is.finite(X)
verbose && cat(verbose, "Distribution of units with known covariates:")
verbose && summary(verbose, hasX)
nbrOfCovariates <- ncol(X)
allCovariates <- seq_len(nbrOfCovariates)
fits <- list()
for (ee in allCovariates) {
verbose && enter(verbose, "Covariate #", ee, " of ", nbrOfCovariates)
# Fit only to units with known length and non-missing data points.
ok <- (hasX[,ee] & okYR)
verbose && cat(verbose, "Distribution of units with known covariates and finite signals:")
verbose && summary(verbose, ok)
# Exclude multi-covariate units
for (ff in setdiff(allCovariates, ee)) {
ok <- ok & !hasX[,ff]
}
verbose && cat(verbose, "Distribution of units with known covariates and finite signals that are exclusively for this covarite:")
verbose && summary(verbose, ok)
# Sanity check
if (sum(ok) == 0) {
throw("Cannot fit target function to covariate, because there are no (finite) data points that are unique to this covariate: ", ee)
}
# Fit effect to single-covariate units
fit <- lowess(X[ok,ee], yR[ok])
class(fit) <- "lowess"
# Not needed anymore
ok <- NULL
fits[[ee]] <- fit
# Not needed anymore
fit <- NULL
verbose && exit(verbose)
} # for (ee ...)
# Remove as many promises as possible
# Not needed anymore
target <- nbrOfCovariates <- allCovariates <- X <- hasX <- yR <- okYR <- NULL
# Create a target prediction function for each covariate
fcns <- vector("list", length(fits))
for (ee in seq_along(fits)) {
fcns[[ee]] <- function(x, ...) {
predict(fits[[ee]], x, ...); # Dispatched predict.lowess().
}
}
verbose && str(verbose, fcns)
verbose && exit(verbose)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
target <- fcns
# Not needed anymore
fcns <- NULL
this$.target <- target
force <- FALSE
verbose && exit(verbose)
} # if (force || ...)
target
}, private=TRUE)
###########################################################################/**
# @RdocMethod process
#
# @title "Normalizes the data set"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Additional arguments passed to
# @see "aroma.light::normalizeFragmentLength" (only for advanced users).}
# \item{force}{If @TRUE, data already normalized is re-normalized,
# otherwise not.}
# \item{verbose}{See @see "R.utils::Verbose".}
# }
#
# \value{
# Returns a @double @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#*/###########################################################################
setMethodS3("process", "AdditiveCovariatesNormalization", function(this, ..., force=FALSE, verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
verbose && enter(verbose, "Normalizing set for PCR fragment-length effects")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Already done?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (!force && isDone(this)) {
verbose && cat(verbose, "Already normalized")
verbose && enter(verbose, "Getting output data set")
outputSet <- getOutputDataSet(this, verbose=less(verbose, 10))
verbose && exit(verbose)
verbose && exit(verbose)
return(invisible(outputSet))
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Setup
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Get input data set
ces <- getInputDataSet(this)
verbose && enter(verbose, "Identifying SNP and CN units")
# Get SNP & CN units
cdf <- getCdf(ces)
types <- getUnitTypes(cdf, verbose=less(verbose,1))
verbose && print(verbose, table(types))
subsetToUpdate <- which(types == 2 | types == 5)
# Not needed anymore
types <- NULL
verbose && cat(verbose, "subsetToUpdate:")
verbose && str(verbose, subsetToUpdate)
verbose && exit(verbose)
verbose && enter(verbose, "Identifying the subset used to fit normalization function(s)")
# Get subset to fit
subsetToFit <- getSubsetToFit(this, verbose=less(verbose))
verbose && str(verbose, subsetToFit)
verbose && exit(verbose)
shift <- this$shift
verbose && cat(verbose, "Shift: ", shift)
onMissing <- this$.onMissing
verbose && cat(verbose, "onMissing: ", onMissing)
# Get (and create) the output path
path <- getPath(this)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Normalize each array
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
X <- NULL
targetFcns <- NULL
# map <- NULL
cellMatrixMap <- NULL
nbrOfArrays <- length(ces)
for (kk in seq_len(nbrOfArrays)) {
ce <- ces[[kk]]
verbose && enter(verbose, sprintf("Array #%d of %d ('%s')",
kk, nbrOfArrays, getName(ce)))
filename <- getFilename(ce)
pathname <- filePath(path, filename)
if (!force && isFile(pathname)) {
verbose && cat(verbose, "Already normalized. Skipping.")
ceN <- fromFile(ce, pathname)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(ce, "SnpChipEffectFile")) {
ceN$mergeStrands <- ce$mergeStrands
if (inherits(ce, "CnChipEffectFile")) {
ceN$combineAlleles <- ce$combineAlleles
}
}
# CDF inheritance
setCdf(ceN, cdf)
verbose && exit(verbose)
next
}
# Get unit-to-cell? (for optimized reading)
# if (is.null(map)) {
# # Only loaded if really needed.
# verbose && enter(verbose, "Retrieving unit-to-cell map for all arrays")
# map <- getUnitGroupCellMap(ce, units=subsetToUpdate, verbose=less(verbose))
# verbose && str(verbose, map)
# verbose && exit(verbose)
# }
if (is.null(X)) {
# For the subset to be fitted, get PCR fragment lengths (for all covariates)
X <- getCovariates(this, units=subsetToUpdate, verbose=less(verbose,5))
verbose && summary(verbose, X)
# Get the index in the data vector of subset to be fitted.
# Note: match() only returns first match, which is why we do
# it this way.
subset <- match(subsetToUpdate, subsetToFit)
subset <- subset[!is.na(subset)]
subset <- match(subsetToFit[subset], subsetToUpdate)
verbose && str(verbose, subset)
}
if (is.null(targetFcns)) {
# Only loaded if really needed.
# Retrieve/calculate the target function
targetFcns <- getTargetFunctions(this, verbose=less(verbose))
}
if (is.null(cellMatrixMap)) {
verbose && enter(verbose, "Getting cell matrix map")
cellMatrixMap <- getUnitGroupCellMatrixMap(ce, units=subsetToUpdate, verbose=less(verbose, 10))
verbose && str(verbose, cellMatrixMap)
verbose && exit(verbose)
}
# Get target log2 signals for all SNPs to be updated
verbose && enter(verbose, "Getting theta estimates")
theta <- extractTheta(ce, units=cellMatrixMap, drop=FALSE, verbose=less(verbose, 5))
verbose && str(verbose, theta)
verbose && summary(verbose, theta)
verbose && exit(verbose)
verbose && enter(verbose, "Calculating total signals")
# Get the total locus signals?
if (ncol(theta) > 1) {
# Row sums with na.rm=TRUE => NAs are treated as zeros.
y <- theta
y[is.na(y)] <- 0
for (cc in 2:ncol(y)) {
y[,1] <- y[,1] + y[,cc]
}
y <- y[,1,drop=TRUE]
} else {
y <- theta[,1,drop=TRUE]
}
verbose && cat(verbose, "Total thetas:")
verbose && str(verbose, y)
verbose && exit(verbose)
# Extract the values to fit the normalization function
verbose && enter(verbose, "Normalizing log2 signals")
# Shift?
if (shift != 0)
y <- y + shift
# Fit on the log2 scale
y <- log2(y)
verbose && cat(verbose, "Log2 signals:")
verbose && str(verbose, y)
yN <- .normalizeFragmentLength(y, fragmentLengths=X,
targetFcns=targetFcns, subsetToFit=subset,
onMissing=onMissing, ...)
verbose && cat(verbose, "Normalized log2 signals:")
verbose && summary(verbose, yN)
# Normalization scale factor for each unit (on the log2 scale)
rho <- y-yN
# Not needed anymore
y <- yN <- NULL
# On the intensity scale
rho <- 2^rho
verbose && cat(verbose, "Normalization scale factors:")
verbose && summary(verbose, rho)
# Sanity check
stopifnot(length(rho) == nrow(theta))
# Normalize the theta:s (on the intensity scale)
ok <- which(is.finite(rho))
verbose && str(verbose, ok)
theta[ok,] <- theta[ok,]/rho[ok]
# Not needed anymore
ok <- rho <- NULL
verbose && cat(verbose, "Normalized thetas:")
verbose && str(verbose, theta)
verbose && summary(verbose, theta)
verbose && exit(verbose)
# Write to a temporary file (allow rename of existing one if forced)
isFile <- (force && isFile(pathname))
pathnameT <- pushTemporaryFile(pathname, isFile=isFile, verbose=verbose)
# Create CEL file to store results, if missing
verbose && enter(verbose, "Creating CEL file for results, if missing")
ceN <- createFrom(ce, filename=pathnameT, path=NULL, verbose=less(verbose))
verbose && exit(verbose)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(ce, "SnpChipEffectFile")) {
ceN$mergeStrands <- ce$mergeStrands
if (inherits(ce, "CnChipEffectFile")) {
ceN$combineAlleles <- ce$combineAlleles
}
}
# CDF inheritance
setCdf(ceN, cdf)
verbose && enter(verbose, "Storing normalized signals")
ok <- which(is.finite(cellMatrixMap))
cells <- cellMatrixMap[ok]
data <- theta[ok]
# Not needed anymore
ok <- theta <- NULL
verbose2 <- -as.integer(verbose) - 5
pathnameN <- getPathname(ceN)
.updateCel(pathnameN, indices=cells, intensities=data, verbose=verbose2)
# Not needed anymore
cells <- data <- NULL
verbose && exit(verbose)
# Rename temporary file
popTemporaryFile(pathnameT, verbose=verbose)
## Create checksum
ceNZ <- getChecksumFile(pathname)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
} # for (kk in ...)
# Create the output set
outputSet <- getOutputDataSet(this, verbose=less(verbose,5))
verbose && exit(verbose)
outputSet
})
HenrikBengtsson/aroma.affymetrix documentation built on Feb. 20, 2024, 9:07 p.m.
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###########################################################################/**
# @RdocClass AdditiveCovariatesNormalization
#
# @title "The AdditiveCovariatesNormalization class"
#
# \description{
# @classhierarchy
#
# This class represents a normalization method that corrects for
# GC-content effects on copy-number chip-effect estimates.
# }
#
# @synopsis
#
# \arguments{
# \item{dataSet}{A @see "SnpChipEffectSet".}
# \item{...}{Additional arguments passed to the constructor of
# @see "ChipEffectTransform".}
# \item{target}{(Optional) A @character string or a @function
# specifying what to normalize toward.}
# \item{subsetToFit}{The units from which the normalization curve should
# be estimated. If @NULL, all are considered.}
# \item{onMissing}{Specifies how to normalize units for which the
# GC contents are unknown.}
# \item{shift}{An optional amount the data points should be shifted
# (translated).}
# }
#
# \section{Fields and Methods}{
# @allmethods "public"
# }
#
# \details{
# For SNPs, the normalization function is estimated based on the total
# chip effects, i.e. the sum of the allele signals. The normalizing
# is done by rescale the chip effects on the intensity scale such that
# the mean of the total chip effects are the same across samples for
# any given GC content. For allele-specific estimates, both alleles
# are always rescaled by the same amount. Thus, when normalizing
# allele-specific chip effects, the total chip effects is change, but not
# the relative allele signal, e.g. the allele B frequency.
# }
#
# @author
#*/###########################################################################
setConstructorS3("AdditiveCovariatesNormalization", function(dataSet=NULL, ..., target=NULL, subsetToFit="-XY", shift=0, onMissing=c("median", "ignore")) {
extraTags <- NULL
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'dataSet':
if (!is.null(dataSet)) {
dataSets <- Arguments$getInstanceOf(dataSet, "SnpChipEffectSet")
}
# Argument 'target':
if (!is.null(target)) {
if (is.function(target)) {
target <- list(target)
}
if (is.character(target)) {
if (target == "zero") {
} else {
throw("Unknown value of argument 'target': ", target)
}
} else if (is.list(target)) {
# Validate each element
for (kk in seq_along(target)) {
if (!is.function(target[[kk]])) {
throw("One element in 'target' is not a function: ",
class(target[[kk]])[1])
}
}
} else {
throw("Unknown value of argument 'target': ", class(target)[1])
}
}
# Argument 'subsetToFit':
if (is.null(subsetToFit)) {
} else if (is.character(subsetToFit)) {
if (subsetToFit %in% c("-X", "-Y", "-XY")) {
} else {
throw("Unknown value of argument 'subsetToFit': ", subsetToFit)
}
extraTags <- c(extraTags, subsetToFit=subsetToFit)
} else {
unf <- getUnitNamesFile(dataSet)
subsetToFit <- Arguments$getIndices(subsetToFit, max=nbrOfUnits(unf))
subsetToFit <- unique(subsetToFit)
subsetToFit <- sort(subsetToFit)
}
# Argument 'shift':
shift <- Arguments$getDouble(shift, disallow=c("NA", "NaN", "Inf"))
# Argument 'onMissing':
onMissing <- match.arg(onMissing)
extend(ChipEffectTransform(dataSet, ...), "AdditiveCovariatesNormalization",
.subsetToFit = subsetToFit,
"cached:.target" = target,
.onMissing = onMissing,
.extraTags = extraTags,
shift = shift
)
})
setMethodS3("getAsteriskTags", "AdditiveCovariatesNormalization", function(this, collapse=NULL, ...) {
tags <- NextMethod("getAsteriskTags", collapse=collapse)
# Extra tags?
tags <- c(tags, this$.extraTags)
# Add class-specific tags
shift <- as.integer(round(this$shift))
if (shift != 0) {
tags <- c(tags, sprintf("%+d", shift))
}
# Collapse?
tags <- paste(tags, collapse=collapse)
tags
}, protected=TRUE)
setMethodS3("getParameters", "AdditiveCovariatesNormalization", function(this, expand=TRUE, ...) {
# Get parameters from super class
params <- NextMethod("getParameters", expand=expand)
# Get parameters of this class
params <- c(params, list(
subsetToFit = this$.subsetToFit,
onMissing = this$.onMissing,
.target = this$.target,
shift = this$shift
))
# Expand?
if (expand) {
subsetToFit <- getSubsetToFit(this)
}
params
}, protected=TRUE)
setMethodS3("getCdf", "AdditiveCovariatesNormalization", function(this, ...) {
inputDataSet <- getInputDataSet(this)
getCdf(inputDataSet)
})
setMethodS3("getOutputDataSet00", "AdditiveCovariatesNormalization", function(this, ..., verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
verbose && enter(verbose, "Getting input data set")
ces <- getInputDataSet(this)
verbose && cat(verbose, "Class: ", class(ces)[1])
verbose && exit(verbose)
verbose && enter(verbose, "Getting output data set for ", class(this)[1])
args <- list(generic="getOutputDataSet", object=this, ...)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Inherit certain arguments from the input data set
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (inherits(ces, "CnChipEffectSet"))
args$combineAlleles <- ces$combineAlleles
if (inherits(ces, "SnpChipEffectSet"))
args$mergeStrands <- ces$mergeStrands
verbose && cat(verbose, "Calling NextMethod() with arguments:")
verbose && str(verbose, args)
args$verbose <- less(verbose, 10)
res <- do.call(NextMethod, args)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(res, "SnpChipEffectSet")) {
verbose && enter(verbose, "Carrying down parameters for ", class(res)[1])
res$mergeStrands <- ces$mergeStrands
if (inherits(res, "CnChipEffectSet")) {
res$combineAlleles <- ces$combineAlleles
}
verbose && exit(verbose)
}
# Let the set update itself
update2(res, verbose=less(verbose, 1))
verbose && exit(verbose)
res
}, protected=TRUE)
setMethodS3("getCovariates", "AdditiveCovariatesNormalization", abstract=TRUE, protected=TRUE)
setMethodS3("getSubsetToFit", "AdditiveCovariatesNormalization", function(this, force=FALSE, ..., verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
# Cached?
units <- this$.units
if (!is.null(units) && !force)
return(units)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Identifying all potential units, i.e. SNPs and CN probes
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
verbose && enter(verbose, "Identifying units that are SNP and CN probes")
# Get SNP information
cdf <- getCdf(this)
nbrOfUnits <- nbrOfUnits(cdf)
# Identify all SNP and CN units (==potential units to be fitted)
verbose && enter(verbose, "Identifying SNPs and CN probes")
types <- getUnitTypes(cdf, verbose=less(verbose,1))
verbose && print(verbose, table(types))
units <- which(types == 2 | types == 5)
# Not needed anymore
types <- NULL
verbose && cat(verbose, "units:")
verbose && str(verbose, units)
verbose && exit(verbose)
onMissing <- this$.onMissing
if (onMissing == "ignore") {
# Keep only those for which we have annotation data covariates on
# for at least one covariate
verbose && enter(verbose, "Reading annotation data covariates")
X <- getCovariates(this, units=units, verbose=less(verbose,5))
keep <- rep(FALSE, nrow(X))
for (ee in seq_len(ncol(X))) {
keep <- keep | is.finite(X[,ee])
}
units <- units[keep]
verbose && printf(verbose, "Number of SNP/CN units without finite covariates: %d out of %d (%.1f%%)\n", sum(!keep), length(keep), 100*sum(!keep)/length(keep))
verbose && exit(verbose)
# Not needed anymore
keep <- X <- NULL
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Subset with a prespecified set of units?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
subsetToFit <- this$.subsetToFit
if (is.character(subsetToFit)) {
if (subsetToFit %in% c("-X", "-Y", "-XY")) {
verbose && enter(verbose, "Identify subset of units from genome information")
verbose && cat(verbose, "subsetToFit: ", subsetToFit)
# Look up in cache
subset <- this$.subsetToFitExpanded
if (is.null(subset)) {
# Get the genome information (throws an exception if missing)
gi <- getGenomeInformation(cdf)
verbose && print(verbose, gi)
# Identify units to be excluded
if (subsetToFit == "-X") {
subset <- getUnitsOnChromosomes(gi, 23, .checkArgs=FALSE)
} else if (subsetToFit == "-Y") {
subset <- getUnitsOnChromosomes(gi, 24, .checkArgs=FALSE)
} else if (subsetToFit == "-XY") {
subset <- getUnitsOnChromosomes(gi, 23:24, .checkArgs=FALSE)
}
verbose && cat(verbose, "Units to exclude: ")
verbose && str(verbose, subset)
# The units to keep
subset <- setdiff(seq_len(nbrOfUnits), subset)
verbose && cat(verbose, "Units to include: ")
verbose && str(verbose, subset)
# Store
this$.subsetToFitExpanded <- subset
}
subsetToFit <- subset
# Not needed anymore
subset <- NULL
verbose && exit(verbose)
}
}
if (!is.null(subsetToFit)) {
# A fraction subset?
if (length(subsetToFit) == 1 && 0 < subsetToFit && subsetToFit < 1) {
keep <- seq(from=1, to=length(units), length=subsetToFit*length(units))
} else {
keep <- which(units %in% subsetToFit)
}
verbose && enter(verbose, "Reading annotation data covariates")
X <- getCovariates(this, units=units, verbose=less(verbose,5))
verbose && str(verbose, X)
verbose && exit(verbose)
# Make sure to keep data points at the tails too
extremeUnits <- c()
for (ee in seq_len(ncol(X))) {
extremeUnits <- c(extremeUnits, which.min(X[,ee]), which.max(X[,ee]))
}
# Not needed anymore
X <- NULL
keep <- c(keep, extremeUnits)
keep <- unique(keep)
# Now filter
units <- units[keep]
# Not needed anymore
keep <- NULL
}
# Sort units
units <- sort(units)
# Assert correctness
units <- Arguments$getIndices(units, max=nbrOfUnits)
# Cache
this$.units <- units
verbose && exit(verbose)
units
}, private=TRUE)
setMethodS3("getTargetFunctions", "AdditiveCovariatesNormalization", function(this, ..., force=FALSE, verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
target <- this$.target
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Handling argument 'force'
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# AD HOC: If forced, make sure to use the original target argument
if (force) {
if (!is.null(target)) {
targetType <- attr(target, "targetType")
if (!is.null(targetType))
target <- targetType
# Not needed anymore
targetType <- NULL
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Predefined target functions?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (is.character(target)) {
verbose && enter(verbose, "Setting up predefined target functions")
targetType <- target
verbose && cat(verbose, "Target type: ", targetType)
# Infer the number of covariates
X <- getCovariates(this, units=1:5)
nbrOfCovariates <- ncol(X)
# Not needed anymore
X <- NULL
if (identical(targetType, "zero")) {
target <- rep(list(function(...) log2(2200)), nbrOfCovariates)
} else {
throw("Unknown target function: ", targetType)
}
# Store the original target type in an attribute
attr(target, "targetType") <- targetType
this$.target <- target
force <- FALSE
verbose && exit(verbose)
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Target functions based on the average array?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (force || is.null(target)) {
# Get target set
ces <- getInputDataSet(this)
verbose && enter(verbose, "Getting average signal across arrays")
ceR <- getAverageFile(ces, force=force, verbose=less(verbose))
# Not needed anymore
ces <- NULL; # Not needed anymore
verbose && exit(verbose)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
# Get units to fit
units <- getSubsetToFit(this)
verbose && cat(verbose, "Units:")
verbose && str(verbose, units)
# Get target signals for SNPs
yR <- extractTheta(ceR, units=units, verbose=less(verbose, 5))
# Not needed anymore
ceR <- NULL; # Not needed anymore
verbose && cat(verbose, "(Allele-specific) thetas:")
verbose && str(verbose, yR)
# If more than one theta per unit, sum them up to get the total signal
if (ncol(yR) > 1) {
# Row sums with na.rm=TRUE => NAs are treated as zeros.
yR[is.na(yR)] <- 0
for (cc in 2:ncol(yR)) {
yR[,1] <- yR[,1] + yR[,cc]
}
}
yR <- yR[,1,drop=TRUE]
verbose && cat(verbose, "Total thetas:")
verbose && str(verbose, yR)
verbose && cat(verbose, "Summary of total signals (on the intensity scale):")
verbose && summary(verbose, yR)
# Shift?
shift <- this$shift
if (shift != 0) {
verbose && printf(verbose, "Applying shift: %+f\n", shift)
yR <- yR + shift
verbose && cat(verbose, "Summary of shifted signals (on the intensity scale):")
verbose && summary(verbose, yR)
}
yR <- log2(yR)
verbose && cat(verbose, "Signals:")
verbose && str(verbose, yR)
verbose && cat(verbose, "Summary of signals (on the log2 scale):")
verbose && summary(verbose, yR)
# Get PCR fragment lengths for these
X <- getCovariates(this, units=units, verbose=less(verbose,5))
# Not needed anymore
units <- NULL; # Not needed anymore
verbose && cat(verbose, "Annotation data covariates:")
verbose && str(verbose, X)
verbose && cat(verbose, "Summary of covariates:")
verbose && summary(verbose, X)
# Fit lowess function
verbose && enter(verbose, "Fitting target prediction function to each covariate exclusively")
okYR <- is.finite(yR)
verbose && cat(verbose, "Distribution of log2 signals that are finite:")
verbose && summary(verbose, okYR)
hasX <- is.finite(X)
verbose && cat(verbose, "Distribution of units with known covariates:")
verbose && summary(verbose, hasX)
nbrOfCovariates <- ncol(X)
allCovariates <- seq_len(nbrOfCovariates)
fits <- list()
for (ee in allCovariates) {
verbose && enter(verbose, "Covariate #", ee, " of ", nbrOfCovariates)
# Fit only to units with known length and non-missing data points.
ok <- (hasX[,ee] & okYR)
verbose && cat(verbose, "Distribution of units with known covariates and finite signals:")
verbose && summary(verbose, ok)
# Exclude multi-covariate units
for (ff in setdiff(allCovariates, ee)) {
ok <- ok & !hasX[,ff]
}
verbose && cat(verbose, "Distribution of units with known covariates and finite signals that are exclusively for this covarite:")
verbose && summary(verbose, ok)
# Sanity check
if (sum(ok) == 0) {
throw("Cannot fit target function to covariate, because there are no (finite) data points that are unique to this covariate: ", ee)
}
# Fit effect to single-covariate units
fit <- lowess(X[ok,ee], yR[ok])
class(fit) <- "lowess"
# Not needed anymore
ok <- NULL
fits[[ee]] <- fit
# Not needed anymore
fit <- NULL
verbose && exit(verbose)
} # for (ee ...)
# Remove as many promises as possible
# Not needed anymore
target <- nbrOfCovariates <- allCovariates <- X <- hasX <- yR <- okYR <- NULL
# Create a target prediction function for each covariate
fcns <- vector("list", length(fits))
for (ee in seq_along(fits)) {
fcns[[ee]] <- function(x, ...) {
predict(fits[[ee]], x, ...); # Dispatched predict.lowess().
}
}
verbose && str(verbose, fcns)
verbose && exit(verbose)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
target <- fcns
# Not needed anymore
fcns <- NULL
this$.target <- target
force <- FALSE
verbose && exit(verbose)
} # if (force || ...)
target
}, private=TRUE)
###########################################################################/**
# @RdocMethod process
#
# @title "Normalizes the data set"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Additional arguments passed to
# @see "aroma.light::normalizeFragmentLength" (only for advanced users).}
# \item{force}{If @TRUE, data already normalized is re-normalized,
# otherwise not.}
# \item{verbose}{See @see "R.utils::Verbose".}
# }
#
# \value{
# Returns a @double @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#*/###########################################################################
setMethodS3("process", "AdditiveCovariatesNormalization", function(this, ..., force=FALSE, verbose=FALSE) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'verbose':
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
verbose && enter(verbose, "Normalizing set for PCR fragment-length effects")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Already done?
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (!force && isDone(this)) {
verbose && cat(verbose, "Already normalized")
verbose && enter(verbose, "Getting output data set")
outputSet <- getOutputDataSet(this, verbose=less(verbose, 10))
verbose && exit(verbose)
verbose && exit(verbose)
return(invisible(outputSet))
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Setup
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Get input data set
ces <- getInputDataSet(this)
verbose && enter(verbose, "Identifying SNP and CN units")
# Get SNP & CN units
cdf <- getCdf(ces)
types <- getUnitTypes(cdf, verbose=less(verbose,1))
verbose && print(verbose, table(types))
subsetToUpdate <- which(types == 2 | types == 5)
# Not needed anymore
types <- NULL
verbose && cat(verbose, "subsetToUpdate:")
verbose && str(verbose, subsetToUpdate)
verbose && exit(verbose)
verbose && enter(verbose, "Identifying the subset used to fit normalization function(s)")
# Get subset to fit
subsetToFit <- getSubsetToFit(this, verbose=less(verbose))
verbose && str(verbose, subsetToFit)
verbose && exit(verbose)
shift <- this$shift
verbose && cat(verbose, "Shift: ", shift)
onMissing <- this$.onMissing
verbose && cat(verbose, "onMissing: ", onMissing)
# Get (and create) the output path
path <- getPath(this)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Normalize each array
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
X <- NULL
targetFcns <- NULL
# map <- NULL
cellMatrixMap <- NULL
nbrOfArrays <- length(ces)
for (kk in seq_len(nbrOfArrays)) {
ce <- ces[[kk]]
verbose && enter(verbose, sprintf("Array #%d of %d ('%s')",
kk, nbrOfArrays, getName(ce)))
filename <- getFilename(ce)
pathname <- filePath(path, filename)
if (!force && isFile(pathname)) {
verbose && cat(verbose, "Already normalized. Skipping.")
ceN <- fromFile(ce, pathname)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(ce, "SnpChipEffectFile")) {
ceN$mergeStrands <- ce$mergeStrands
if (inherits(ce, "CnChipEffectFile")) {
ceN$combineAlleles <- ce$combineAlleles
}
}
# CDF inheritance
setCdf(ceN, cdf)
verbose && exit(verbose)
next
}
# Get unit-to-cell? (for optimized reading)
# if (is.null(map)) {
# # Only loaded if really needed.
# verbose && enter(verbose, "Retrieving unit-to-cell map for all arrays")
# map <- getUnitGroupCellMap(ce, units=subsetToUpdate, verbose=less(verbose))
# verbose && str(verbose, map)
# verbose && exit(verbose)
# }
if (is.null(X)) {
# For the subset to be fitted, get PCR fragment lengths (for all covariates)
X <- getCovariates(this, units=subsetToUpdate, verbose=less(verbose,5))
verbose && summary(verbose, X)
# Get the index in the data vector of subset to be fitted.
# Note: match() only returns first match, which is why we do
# it this way.
subset <- match(subsetToUpdate, subsetToFit)
subset <- subset[!is.na(subset)]
subset <- match(subsetToFit[subset], subsetToUpdate)
verbose && str(verbose, subset)
}
if (is.null(targetFcns)) {
# Only loaded if really needed.
# Retrieve/calculate the target function
targetFcns <- getTargetFunctions(this, verbose=less(verbose))
}
if (is.null(cellMatrixMap)) {
verbose && enter(verbose, "Getting cell matrix map")
cellMatrixMap <- getUnitGroupCellMatrixMap(ce, units=subsetToUpdate, verbose=less(verbose, 10))
verbose && str(verbose, cellMatrixMap)
verbose && exit(verbose)
}
# Get target log2 signals for all SNPs to be updated
verbose && enter(verbose, "Getting theta estimates")
theta <- extractTheta(ce, units=cellMatrixMap, drop=FALSE, verbose=less(verbose, 5))
verbose && str(verbose, theta)
verbose && summary(verbose, theta)
verbose && exit(verbose)
verbose && enter(verbose, "Calculating total signals")
# Get the total locus signals?
if (ncol(theta) > 1) {
# Row sums with na.rm=TRUE => NAs are treated as zeros.
y <- theta
y[is.na(y)] <- 0
for (cc in 2:ncol(y)) {
y[,1] <- y[,1] + y[,cc]
}
y <- y[,1,drop=TRUE]
} else {
y <- theta[,1,drop=TRUE]
}
verbose && cat(verbose, "Total thetas:")
verbose && str(verbose, y)
verbose && exit(verbose)
# Extract the values to fit the normalization function
verbose && enter(verbose, "Normalizing log2 signals")
# Shift?
if (shift != 0)
y <- y + shift
# Fit on the log2 scale
y <- log2(y)
verbose && cat(verbose, "Log2 signals:")
verbose && str(verbose, y)
yN <- .normalizeFragmentLength(y, fragmentLengths=X,
targetFcns=targetFcns, subsetToFit=subset,
onMissing=onMissing, ...)
verbose && cat(verbose, "Normalized log2 signals:")
verbose && summary(verbose, yN)
# Normalization scale factor for each unit (on the log2 scale)
rho <- y-yN
# Not needed anymore
y <- yN <- NULL
# On the intensity scale
rho <- 2^rho
verbose && cat(verbose, "Normalization scale factors:")
verbose && summary(verbose, rho)
# Sanity check
stopifnot(length(rho) == nrow(theta))
# Normalize the theta:s (on the intensity scale)
ok <- which(is.finite(rho))
verbose && str(verbose, ok)
theta[ok,] <- theta[ok,]/rho[ok]
# Not needed anymore
ok <- rho <- NULL
verbose && cat(verbose, "Normalized thetas:")
verbose && str(verbose, theta)
verbose && summary(verbose, theta)
verbose && exit(verbose)
# Write to a temporary file (allow rename of existing one if forced)
isFile <- (force && isFile(pathname))
pathnameT <- pushTemporaryFile(pathname, isFile=isFile, verbose=verbose)
# Create CEL file to store results, if missing
verbose && enter(verbose, "Creating CEL file for results, if missing")
ceN <- createFrom(ce, filename=pathnameT, path=NULL, verbose=less(verbose))
verbose && exit(verbose)
# Carry over parameters too. AD HOC for now. /HB 2007-01-07
if (inherits(ce, "SnpChipEffectFile")) {
ceN$mergeStrands <- ce$mergeStrands
if (inherits(ce, "CnChipEffectFile")) {
ceN$combineAlleles <- ce$combineAlleles
}
}
# CDF inheritance
setCdf(ceN, cdf)
verbose && enter(verbose, "Storing normalized signals")
ok <- which(is.finite(cellMatrixMap))
cells <- cellMatrixMap[ok]
data <- theta[ok]
# Not needed anymore
ok <- theta <- NULL
verbose2 <- -as.integer(verbose) - 5
pathnameN <- getPathname(ceN)
.updateCel(pathnameN, indices=cells, intensities=data, verbose=verbose2)
# Not needed anymore
cells <- data <- NULL
verbose && exit(verbose)
# Rename temporary file
popTemporaryFile(pathnameT, verbose=verbose)
## Create checksum
ceNZ <- getChecksumFile(pathname)
# Garbage collect
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
} # for (kk in ...)
# Create the output set
outputSet <- getOutputDataSet(this, verbose=less(verbose,5))
verbose && exit(verbose)
outputSet
})
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