R/peaksAlignment.R
In rromoli/flagme: Analysis of Metabolomics GC/MS Data
Defines functions
peaksAlignment
Documented in
peaksAlignment
##' Compression method for peaksAlignment object
##'
##' Compression method for peaksAlignment object
##' @param object peaksAlignment
##' @param verbose logical
##' @param ... further
##' @author MR
##' @import methods
##' @importFrom methods setMethod new
##' @keywords internal
setMethod("compress","peaksAlignment",
function(object,verbose = TRUE, ...) {
if(object@compressed)
{
if (verbose){
cat("[compress.peaksAlignment] Already compressed.\n")}
return(object)
}
#if (verbose)
# before<-sum(ll(object)$KB)
object@r <- as.matrix.csc(1-object@r)
#if (verbose) {
# after<-sum(ll(object)$KB)
# cat("[compress.peaksAlignment] Object is",(before-after),"KB
# smaller.\n")
#}
object@compressed <- TRUE
new("peaksAlignment", object)
})
##' Decompression method for peaksAlignment object
##'
##' Decompression method for peaksAlignment object
##' @param object peaksAlignment object
##' @param verbose dummy
##' @param ... dummy
##' @author MR
##' @import methods
##' @importFrom methods setMethod new
##' @keywords internal
setMethod("decompress", "peaksAlignment",
function(object, verbose = TRUE, ...) {
if(!(object@compressed)) {
if(verbose)
{
cat("[decompress.peaksAlignment] Already decompressed.
\n")
}
return(object)
}
## if (verbose)
## {before<-sum(ll(object)$KB)}
object@r <- 1-(as.matrix(object@r))
## if (verbose) {
## after<-sum(ll(object)$KB)
## cat("[decompress.peaksAlignment] Object
## is",(after-before),"KB larger.\n")
## }
object@compressed <- FALSE
new("peaksAlignment", object)
})
#' Data Structure for pairwise alignment of 2 GCMS samples
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' peaksAlignment is a hold-all data structure of the raw and peak detection
#' data.
#' @param object peaksAlignment object
#' @author Mark Robinson, Riccardo Romoli
#' @export
#' @noRd
setMethod("show","peaksAlignment",
function(object){
cat("An object of class \"", class(object), "\"\n", sep="")
dm <- dim(object@r)
cat(dm[1], "peaks against", dm[2],"peaks:",
nrow(object@v$match), "matched\n")
})
#' Data Structure for pairwise alignment of 2 GCMS samples
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' peaksAlignment is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @name peaksAlignment-class
#' @aliases peaksAlignment-class peaksAlignment-show peaksAlignment-plot
#' peaksAlignment show,peaksAlignment-method plot,peaksAlignment-method
#' plot,peaksAlignment,ANY-method
#' @param d1 matrix of MS intensities for 1st sample (if doing a peak
#' alignment, this contains peak apexes/areas; if doing a profile alignment,
#' this contains scan intensities. Rows are m/z bins, columns are
#' peaks/scans.
#' @param d2 matrix of MS intensities for 2nd sample
#' @param t1 vector of retention times for 1st sample
#' @param t2 vector of retention times for 2nd sample
#' @param gap gap penalty for dynamic programming algorithm. Not used if
#' \code{type=2}
#' @param D time window (on same scale as retention time differences,
#' \code{t1}
#' and \code{t2}. Default scale is seconds.)
#' @param timedf list (length = the number of pairwise alignments) of matrices
#' giving the expected time differences expected at each pair of peaks used
#' with \code{usePeaks}=\code{TRUE}.
#' @param df integer, how far from the diagonal to go to calculate the
#' similarity of peaks. Smaller value should run faster, but be careful not to
#' choose too low.
#' @param verbose logical, whether to print out info.
#' @param usePeaks logical, \code{TRUE} uses \code{peakdata} list,
#' \code{FALSE}
#' uses \code{rawdata} list for computing similarity.
#' @param compress logical, whether to compress the similarity matrix into a
#' sparse format.
#' @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}
#' @return \code{peaksAlignment} object
#' @author Mark Robinson, Riccardo Romoli
#' @seealso \code{\link{peaksDataset}}, \code{\link{clusterAlignment}}
#' @references Mark D Robinson (2008). Methods for the analysis of gas
#' chromatography - mass spectrometry data \emph{PhD dissertation} University
#' of Melbourne.
#' @keywords classes
#' @examples
#'
#' ## see clusterAlignment, it calls peaksAlignment
#'
#' ## Not Run:
#' files <- list.files(path = paste(find.package("gcspikelite"), "data",
#' sep = "/"),"CDF", full = TRUE)
#' data <- peaksDataset(files[1:2], mz = seq(50, 550), rtrange = c(7.5, 8.5))
#' ## create settings object
#' mfp <- xcms::MatchedFilterParam(fwhm = 10, snthresh = 5)
#' cwt <- xcms::CentWaveParam(snthresh = 3, ppm = 3000, peakwidth = c(3, 40),
#' prefilter = c(3, 100), fitgauss = FALSE, integrate = 2, noise = 0,
#' extendLengthMSW = TRUE, mzCenterFun = "wMean")
#' data <- addXCMSPeaks(files[1:2], data, settings = mfp)
#' data
#' plotChrom(data, rtrange=c(7.5, 10.5), runs=c(1:2))
#'
#' ## align two chromatogram
#' pA <- peaksAlignment(data@peaksdata[[1]], data@peaksdata[[2]],
#' data@peaksrt[[1]], data@peaksrt[[2]], D = 50,
#' metric = 3, compress = FALSE, type = 2, penality = 0.2)
#'
#' plotAlignment(pA)
#' pA@v$match
#'
#' par(mfrow=c(2,1))
#' plot(data@peaksdata[[1]][,15], type = 'h', main = paste(data@peaksrt[[1]][[15]]))
#' plot(data@peaksdata[[2]][,17], type = 'h',
#' main = paste(data@peaksrt[[2]][[17]]))
#' ## End (Not Run)
#'
#' @export
peaksAlignment <- function(d1, d2, t1, t2, gap = 0.5, D = 50,
timedf = NULL, df = 30, verbose = TRUE,
usePeaks = TRUE, compress = TRUE, metric = 2,
type = 2, penality = 0.2) {
## r <- switch(metric,
## normDotProduct(d1,d2,t1,t2,D=D,
## df=df+abs(ncol(d1)-ncol(d2)),
## timedf=timedf,verbose=verbose), # metric=1
## cos.ndp.rank(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metricic=2
## euclidean(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose),# metric=3
## soai(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metric=4
## manhattan(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metric=5
## window.metric.c(d1,d2,mzind=mzind,df=df,w=w) # metric=6
## )
## if(metric == 1) # why?
## {
## D <- D/100
## }
r <- switch(metric,
normDotProduct(d1, d2, t1, t2, D = D,
df = df + abs(ncol(d1) - ncol(d2)),
timedf = timedf, verbose = verbose),
ndpRT(s1 = d1, s2 = d2, t1, t2, D = D),
corPrt(d1, d2, t1, t2, D = D, penality = penality)
)
r[is.nan(r)] <- 1 ## remove NaN
if (type == 1) {
if(verbose)
cat("[peaksAlignment] Comparing", ncol(d1), "peaks to",
ncol(d2), "peaks -- gap=", gap, "D=", D, ', metric=',
metric, ', type=', type, "\n")
v <- dp(r, gap = gap, verbose = verbose) # dynamic programming
}
if (type == 2) {
if(verbose)
cat("[peaksAlignment] Comparing", ncol(d1), "peaks to",
ncol(d2), "peaks -- D=", D, "seconds,", 'metric=',
metric, ', type=', type, '\n')
v <- dynRT(S = r) # RR, modified to be used with normDotProduct()
}
v$match <- v$match[!is.na(v$match[,2]), ] # remove non-matched peaks
sim <- 0
for(i in 1:nrow(v$match)) {
sim <- sim + r[v$match[i, 1], v$match[i, 2]]#
}
sim <- sim / nrow(v$match)
if(verbose) {
cat("[peaksAlignment] ", nrow(v$match), "matched. Similarity=",
sim, "\n")
}
object <- new("peaksAlignment", v = v, r = r, dist = sim,
compressed = FALSE, gap = gap, D = D)
if(compress) {
compress(object)
} else {
object
}
}
#' Plotting functions for GCMS data objects
#'
#' Plot an object of \code{\linkS4class{peaksAlignment}}
#'
#' The similarity matrix is plotted and optionally, the set of matching peaks.
#' \code{clusterAlignment} objects are just a collection of all pairwise
#' \code{peakAlignment} objects.
#'
#' @title plotAlignment
#' @param object a \code{clusterAlignment} object
#' @param xlab x-axis label
#' @param ylab y-axis label
#' @param plotMatches logical, whether to plot matches
#' @param matchPch match plotting character
#' @param matchLwd match line width
#' @param matchCex match character expansion factor
#' @param matchCol match colour
#' @param col vector of colours for colourscale
#' @param breaks vector of breaks for colourscale
#' @param ... further arguments passed to \code{image}
#' @return plot an object of class \code{\linkS4class{peaksAlignment}}
#' @author Mark Robinson
#' @seealso \code{\link{peaksAlignment}} \code{\link{plotAlignment}}
#' @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)
#' files <- list.files(path = paste(find.package("gcspikelite"), "data",
#' sep = "/"),"CDF", full = TRUE)
#' data <- peaksDataset(files[1:2], mz = seq(50, 550), rtrange = c(7.5, 8.5))
#' ## create settings object
#' mfp <- xcms::MatchedFilterParam(fwhm = 10, snthresh = 5)
#' cwt <- xcms::CentWaveParam(snthresh = 3, ppm = 3000, peakwidth = c(3, 40),
#' prefilter = c(3, 100), fitgauss = FALSE, integrate = 2, noise = 0,
#' extendLengthMSW = TRUE, mzCenterFun = "wMean")
#' data <- addXCMSPeaks(files[1:2], data, settings = mfp)
#' data
#' ## image plot
#' plotChrom(data, rtrange = c(7.5,8.5), plotPeaks = TRUE, plotPeakLabels =TRUE)
#'
#' ## align two chromatogram
#' pA <- peaksAlignment(data@peaksdata[[1]], data@peaksdata[[2]],
#' data@peaksrt[[1]], data@peaksrt[[2]], D = 50,
#' compress = FALSE, type = 1, metric = 1,
#' gap = 0.5)
#' plotAlignment(pA)
#'
#' @importFrom graphics image points
#' @export
setMethod("plotAlignment", "peaksAlignment",
function(object, xlab = "Peaks - run 1",
ylab = "Peaks - run 2", plotMatches = TRUE,
matchPch = 19, matchLwd = 3, matchCex = 0.5,
matchCol = "black",
## col=colorpanel(50,"black", "blue","white"),#
col = colorpanel(50,'white', "green","navyblue"),
breaks = seq(0, 1, length = 51), ...)
{
if(object@compressed)
{
object <- decompress(object, ...)
}
r <- object@r
image(1:nrow(r), 1:ncol(r), r, col = col, xlab = xlab,
ylab = ylab, ...)
if(plotMatches)
{
points(object@v$match, pch = matchPch, col = matchCol,
cex = matchCex, type = "b", lwd = matchLwd)
}
}
)
rromoli/flagme documentation built on Feb. 10, 2023, 12:59 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions peaksAlignment
Documented in peaksAlignment
##' Compression method for peaksAlignment object
##'
##' Compression method for peaksAlignment object
##' @param object peaksAlignment
##' @param verbose logical
##' @param ... further
##' @author MR
##' @import methods
##' @importFrom methods setMethod new
##' @keywords internal
setMethod("compress","peaksAlignment",
function(object,verbose = TRUE, ...) {
if(object@compressed)
{
if (verbose){
cat("[compress.peaksAlignment] Already compressed.\n")}
return(object)
}
#if (verbose)
# before<-sum(ll(object)$KB)
object@r <- as.matrix.csc(1-object@r)
#if (verbose) {
# after<-sum(ll(object)$KB)
# cat("[compress.peaksAlignment] Object is",(before-after),"KB
# smaller.\n")
#}
object@compressed <- TRUE
new("peaksAlignment", object)
})
##' Decompression method for peaksAlignment object
##'
##' Decompression method for peaksAlignment object
##' @param object peaksAlignment object
##' @param verbose dummy
##' @param ... dummy
##' @author MR
##' @import methods
##' @importFrom methods setMethod new
##' @keywords internal
setMethod("decompress", "peaksAlignment",
function(object, verbose = TRUE, ...) {
if(!(object@compressed)) {
if(verbose)
{
cat("[decompress.peaksAlignment] Already decompressed.
\n")
}
return(object)
}
## if (verbose)
## {before<-sum(ll(object)$KB)}
object@r <- 1-(as.matrix(object@r))
## if (verbose) {
## after<-sum(ll(object)$KB)
## cat("[decompress.peaksAlignment] Object
## is",(after-before),"KB larger.\n")
## }
object@compressed <- FALSE
new("peaksAlignment", object)
})
#' Data Structure for pairwise alignment of 2 GCMS samples
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' peaksAlignment is a hold-all data structure of the raw and peak detection
#' data.
#' @param object peaksAlignment object
#' @author Mark Robinson, Riccardo Romoli
#' @export
#' @noRd
setMethod("show","peaksAlignment",
function(object){
cat("An object of class \"", class(object), "\"\n", sep="")
dm <- dim(object@r)
cat(dm[1], "peaks against", dm[2],"peaks:",
nrow(object@v$match), "matched\n")
})
#' Data Structure for pairwise alignment of 2 GCMS samples
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' peaksAlignment is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @name peaksAlignment-class
#' @aliases peaksAlignment-class peaksAlignment-show peaksAlignment-plot
#' peaksAlignment show,peaksAlignment-method plot,peaksAlignment-method
#' plot,peaksAlignment,ANY-method
#' @param d1 matrix of MS intensities for 1st sample (if doing a peak
#' alignment, this contains peak apexes/areas; if doing a profile alignment,
#' this contains scan intensities. Rows are m/z bins, columns are
#' peaks/scans.
#' @param d2 matrix of MS intensities for 2nd sample
#' @param t1 vector of retention times for 1st sample
#' @param t2 vector of retention times for 2nd sample
#' @param gap gap penalty for dynamic programming algorithm. Not used if
#' \code{type=2}
#' @param D time window (on same scale as retention time differences,
#' \code{t1}
#' and \code{t2}. Default scale is seconds.)
#' @param timedf list (length = the number of pairwise alignments) of matrices
#' giving the expected time differences expected at each pair of peaks used
#' with \code{usePeaks}=\code{TRUE}.
#' @param df integer, how far from the diagonal to go to calculate the
#' similarity of peaks. Smaller value should run faster, but be careful not to
#' choose too low.
#' @param verbose logical, whether to print out info.
#' @param usePeaks logical, \code{TRUE} uses \code{peakdata} list,
#' \code{FALSE}
#' uses \code{rawdata} list for computing similarity.
#' @param compress logical, whether to compress the similarity matrix into a
#' sparse format.
#' @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}
#' @return \code{peaksAlignment} object
#' @author Mark Robinson, Riccardo Romoli
#' @seealso \code{\link{peaksDataset}}, \code{\link{clusterAlignment}}
#' @references Mark D Robinson (2008). Methods for the analysis of gas
#' chromatography - mass spectrometry data \emph{PhD dissertation} University
#' of Melbourne.
#' @keywords classes
#' @examples
#'
#' ## see clusterAlignment, it calls peaksAlignment
#'
#' ## Not Run:
#' files <- list.files(path = paste(find.package("gcspikelite"), "data",
#' sep = "/"),"CDF", full = TRUE)
#' data <- peaksDataset(files[1:2], mz = seq(50, 550), rtrange = c(7.5, 8.5))
#' ## create settings object
#' mfp <- xcms::MatchedFilterParam(fwhm = 10, snthresh = 5)
#' cwt <- xcms::CentWaveParam(snthresh = 3, ppm = 3000, peakwidth = c(3, 40),
#' prefilter = c(3, 100), fitgauss = FALSE, integrate = 2, noise = 0,
#' extendLengthMSW = TRUE, mzCenterFun = "wMean")
#' data <- addXCMSPeaks(files[1:2], data, settings = mfp)
#' data
#' plotChrom(data, rtrange=c(7.5, 10.5), runs=c(1:2))
#'
#' ## align two chromatogram
#' pA <- peaksAlignment(data@peaksdata[[1]], data@peaksdata[[2]],
#' data@peaksrt[[1]], data@peaksrt[[2]], D = 50,
#' metric = 3, compress = FALSE, type = 2, penality = 0.2)
#'
#' plotAlignment(pA)
#' pA@v$match
#'
#' par(mfrow=c(2,1))
#' plot(data@peaksdata[[1]][,15], type = 'h', main = paste(data@peaksrt[[1]][[15]]))
#' plot(data@peaksdata[[2]][,17], type = 'h',
#' main = paste(data@peaksrt[[2]][[17]]))
#' ## End (Not Run)
#'
#' @export
peaksAlignment <- function(d1, d2, t1, t2, gap = 0.5, D = 50,
timedf = NULL, df = 30, verbose = TRUE,
usePeaks = TRUE, compress = TRUE, metric = 2,
type = 2, penality = 0.2) {
## r <- switch(metric,
## normDotProduct(d1,d2,t1,t2,D=D,
## df=df+abs(ncol(d1)-ncol(d2)),
## timedf=timedf,verbose=verbose), # metric=1
## cos.ndp.rank(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metricic=2
## euclidean(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose),# metric=3
## soai(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metric=4
## manhattan(d1,d2,t1,t2,D=D,timedf=timedf,
## verbose=verbose), # metric=5
## window.metric.c(d1,d2,mzind=mzind,df=df,w=w) # metric=6
## )
## if(metric == 1) # why?
## {
## D <- D/100
## }
r <- switch(metric,
normDotProduct(d1, d2, t1, t2, D = D,
df = df + abs(ncol(d1) - ncol(d2)),
timedf = timedf, verbose = verbose),
ndpRT(s1 = d1, s2 = d2, t1, t2, D = D),
corPrt(d1, d2, t1, t2, D = D, penality = penality)
)
r[is.nan(r)] <- 1 ## remove NaN
if (type == 1) {
if(verbose)
cat("[peaksAlignment] Comparing", ncol(d1), "peaks to",
ncol(d2), "peaks -- gap=", gap, "D=", D, ', metric=',
metric, ', type=', type, "\n")
v <- dp(r, gap = gap, verbose = verbose) # dynamic programming
}
if (type == 2) {
if(verbose)
cat("[peaksAlignment] Comparing", ncol(d1), "peaks to",
ncol(d2), "peaks -- D=", D, "seconds,", 'metric=',
metric, ', type=', type, '\n')
v <- dynRT(S = r) # RR, modified to be used with normDotProduct()
}
v$match <- v$match[!is.na(v$match[,2]), ] # remove non-matched peaks
sim <- 0
for(i in 1:nrow(v$match)) {
sim <- sim + r[v$match[i, 1], v$match[i, 2]]#
}
sim <- sim / nrow(v$match)
if(verbose) {
cat("[peaksAlignment] ", nrow(v$match), "matched. Similarity=",
sim, "\n")
}
object <- new("peaksAlignment", v = v, r = r, dist = sim,
compressed = FALSE, gap = gap, D = D)
if(compress) {
compress(object)
} else {
object
}
}
#' Plotting functions for GCMS data objects
#'
#' Plot an object of \code{\linkS4class{peaksAlignment}}
#'
#' The similarity matrix is plotted and optionally, the set of matching peaks.
#' \code{clusterAlignment} objects are just a collection of all pairwise
#' \code{peakAlignment} objects.
#'
#' @title plotAlignment
#' @param object a \code{clusterAlignment} object
#' @param xlab x-axis label
#' @param ylab y-axis label
#' @param plotMatches logical, whether to plot matches
#' @param matchPch match plotting character
#' @param matchLwd match line width
#' @param matchCex match character expansion factor
#' @param matchCol match colour
#' @param col vector of colours for colourscale
#' @param breaks vector of breaks for colourscale
#' @param ... further arguments passed to \code{image}
#' @return plot an object of class \code{\linkS4class{peaksAlignment}}
#' @author Mark Robinson
#' @seealso \code{\link{peaksAlignment}} \code{\link{plotAlignment}}
#' @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)
#' files <- list.files(path = paste(find.package("gcspikelite"), "data",
#' sep = "/"),"CDF", full = TRUE)
#' data <- peaksDataset(files[1:2], mz = seq(50, 550), rtrange = c(7.5, 8.5))
#' ## create settings object
#' mfp <- xcms::MatchedFilterParam(fwhm = 10, snthresh = 5)
#' cwt <- xcms::CentWaveParam(snthresh = 3, ppm = 3000, peakwidth = c(3, 40),
#' prefilter = c(3, 100), fitgauss = FALSE, integrate = 2, noise = 0,
#' extendLengthMSW = TRUE, mzCenterFun = "wMean")
#' data <- addXCMSPeaks(files[1:2], data, settings = mfp)
#' data
#' ## image plot
#' plotChrom(data, rtrange = c(7.5,8.5), plotPeaks = TRUE, plotPeakLabels =TRUE)
#'
#' ## align two chromatogram
#' pA <- peaksAlignment(data@peaksdata[[1]], data@peaksdata[[2]],
#' data@peaksrt[[1]], data@peaksrt[[2]], D = 50,
#' compress = FALSE, type = 1, metric = 1,
#' gap = 0.5)
#' plotAlignment(pA)
#'
#' @importFrom graphics image points
#' @export
setMethod("plotAlignment", "peaksAlignment",
function(object, xlab = "Peaks - run 1",
ylab = "Peaks - run 2", plotMatches = TRUE,
matchPch = 19, matchLwd = 3, matchCex = 0.5,
matchCol = "black",
## col=colorpanel(50,"black", "blue","white"),#
col = colorpanel(50,'white', "green","navyblue"),
breaks = seq(0, 1, length = 51), ...)
{
if(object@compressed)
{
object <- decompress(object, ...)
}
r <- object@r
image(1:nrow(r), 1:ncol(r), r, col = col, xlab = xlab,
ylab = ylab, ...)
if(plotMatches)
{
points(object@v$match, pch = matchPch, col = matchCol,
cex = matchCex, type = "b", lwd = matchLwd)
}
}
)
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