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#' Data Structure for "between" alignment of many GCMS samples
#'
#' This function creates a "between" alignment (i.e. comparing merged peaks)
#'
#' \code{betweenAlignment} objects gives the data structure which stores the
#' result of an alignment across several "pseudo" datasets. These pseudo
#' datasets are constructed by merging the "within" alignments.
#'
#' @aliases betweenAlignment betweenAlignment-class betweenAlignment-show show,
#' betweenAlignment-method
#' @param pD a \code{peaksDataset} object
#' @param cAList \code{list} of \code{clusterAlignment} objects, one for each
#' experimental group
#' @param pAList \code{list} of \code{progressiveAlignment} objects, one for
#' each experimental group
#' @param impList \code{list} of imputation lists
#' @param filterMin minimum number of peaks within a merged peak to be kept in
#' the analysis
#' @param gap gap parameter
#' @param D retention time penalty parameter
#' @param usePeaks logical, whether to use peaks (if \code{TRUE}) or the full
#' 2D profile alignment (if \code{FALSE})
#' @param df distance from diagonal to calculate similarity
#' @param verbose logical, whether to print information
#' @param metric numeric, different algorithm to calculate the similarity
#' matrix between two mass spectrum. \code{metric=1} call
#' \code{normDotProduct()}; \code{metric=2} call \code{ndpRT()};
#' \code{metric=3} call \code{corPrt()}
#' @param type numeric, two different type of alignment function
#' @param penality penalization applied to the matching between two mass
#' spectra if \code{(t1-t2)>D}
#' @param compress logical whether to compress the similarity matrix into a
#' sparse format.
#' @return \code{betweenAlignment} object
#' @author Mark Robinson
#' @seealso \code{\link{multipleAlignment}}
#' @references Mark D Robinson (2008). Methods for the analysis of gas
#' chromatography - mass spectrometry data \emph{PhD dissertation} University
#' of Melbourne.
#' @keywords classes
#' @examples
#'
#' require(gcspikelite)
#' ## see 'multipleAlignment'
#' @importFrom stats median
#' @export betweenAlignment
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betweenAlignment <- function(pD, cAList, pAList, impList, filterMin = 1,
gap = 0.7, D = 10, usePeaks = TRUE, df = 30,
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verbose = TRUE, metric = 2, type = 2,
penality = 0.2, compress = FALSE){
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n <- length(pAList)
if(length(filterMin) == 1)
{
filterMin <- rep(filterMin, n)
}
pkd <- vector("list", n)
pkr <- vector("list", n)
filtind <- vector("list", n) # list of filtered indices
newind <- vector("list", n)
for(g in 1:n){
pa <- pAList[[g]]
nm <- length(pa@merges)
runs <- pa@merges[[nm]]$runs
ind <- pa@merges[[nm]]$ind
keep <- rowSums(!is.na(ind)) >= filterMin[g] ## RR
if (verbose)
{
cat("[betweenAlignment]", names(pAList)[g], ": Removing", nrow(ind) - sum(keep),"peaks.\n")
ind <- ind[keep,]
}
newind[[g]] <- lapply(impList[[g]], FUN = function(u,k) u[k,], k = keep)
filtind[[g]] <- ind
rt <- matrix(NA, nrow(ind), ncol(ind))
mz <- pD@mz
peaksdata <- matrix(0, nrow = length(pD@mz), ncol = nrow(ind))
for(j in 1:ncol(ind)){
if(usePeaks)
{
cur.ds <- pD@peaksdata[[runs[j]]]
cur.rt <- pD@peaksrt
}
else
{
cur.ds <- pD@rawdata[[runs[j]]]
cur.rt <- pD@rawrt
}
for(i in 1:nrow(ind)){
if(!is.na(ind[i, j]))
{
peaksdata[,i] <- peaksdata[,i] + cur.ds[,ind[i,j]]
rt[i,j] <- cur.rt[[runs[j]]][ind[i,j]]
}
}
}
pkd[[g]] <- peaksdata
if(!usePeaks)
{
pkd[[g]] <- pkd[[g]] / outer(rep(1, nrow(peaksdata)), rowSums(!is.na(ind)))
} # average over the number of samples
pkr[[g]] <- apply(rt, 1, median, na.rm = TRUE)
}
# wRA <- new("peaksDataset", mz = mz, rawdata = NULL, files = names(cAList),
# rawrt = NULL, peaksdata = pkd, peaksrt = pkr, filtind = filtind)
wRA <- new("peaksDataset",
mz = mz, rawdata = list(NULL), files = names(cAList),
rawrt = list(NULL), peaksdata = pkd, peaksrt = pkr)
## return(wRA)
## filtind <-
## RR
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cA <- clusterAlignment(wRA, runs = 1:n, gap = gap, D = D, df = df,
metric = metric, type = type,
compress = compress, penality = penality) ## bug here with filterMin > 1
pA <- progressiveAlignment(wRA, cA, gap = gap, D = D, df = df,
compress = compress, type = type)
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##
## cA <- clusterAlignment(wRA, 1:n, gap = gap, D = D, df = df, verbose = verbose)
## pA <- progressiveAlignment(wRA, cA, gap = gap, D = D, df = df, verbose = verbose)
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nm <- length(pA@merges)
ind <- pA@merges[[nm]]$ind
groups <- pA@merges[[nm]]$runs
full.runs <- NULL
full.groups <- NULL
full.ind <- matrix(NA, nrow(ind), length(pD@rawdata))
full.newind <- vector("list", 3)
for(i in 1:length(full.newind)){
full.newind[[i]] <- matrix(NA, nrow(ind), length(pD@rawdata))
}
names(full.newind) <- c("apex", "start", "end")
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col <- 1
for(i in 1:length(groups)){
g <- groups[i]
n <- length(pAList[[g]]@merges)
full.runs <- c(full.runs, pAList[[g]]@merges[[n]]$runs)
nr <- length(pAList[[g]]@merges[[n]]$runs)
full.groups <- c(full.groups, rep(wRA@files[g],nr))
this.ind <- filtind[[g]]
rw <- which(!is.na(ind[,i]))
cl <- col:(col + ncol(this.ind) - 1)
full.ind[rw, cl] <- this.ind
if(!is.null(newind[[g]]$apex))
{
for(j in 1:length(full.newind)){
full.newind[[j]] <- newind[[g]][[j]]
}
}
col <- col + ncol(this.ind)
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}
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new("betweenAlignment",
mergedPeaksDataset = wRA,
ind = full.ind,
imputeind = full.newind,
runs = full.runs,
groups = full.groups,
cA = cA,
pA = pA,
filtind = filtind,
newind = newind
)
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}
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#' Show method for "between" alignment of many GCMS samples
#'
#' This function show the results of a "between" alignment
#'
#' \code{betweenAlignment} objects gives the data structure which stores the
#' result of an alignment across several "pseudo" datasets. These pseudo
#' datasets are constructed by merging the "within" alignments.
#' @title betweenAlignment-show
#' @param object
#' @return \code{betweenAlignment} object
#' @author Mark Robinson
#' @export
#' @noRd
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setMethod("show","betweenAlignment",
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function(object) {
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cat("An object of class \"", class(object), "\"\n", sep = "")
cat(length(object@mergedPeaksDataset@peaksrt), "groups:",
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sapply(object@mergedPeaksDataset@peaksrt, length),
"merged peaks\n"
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)
}
)
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