Nothing
R/RmbWorkspace.R
In RMassBank: Workflow to process tandem MS files and build MassBank records
Defines functions
plotMbWorkspaces
newMbWorkspace
loadMsmsWorkspace
newMsmsWorkspace
Documented in
loadMsmsWorkspace
newMbWorkspace
newMsmsWorkspace
plotMbWorkspaces
#' @import methods
#' @importFrom Biobase isVersioned
#' @importFrom Biobase isCurrent
#' @importFrom Biobase classVersion<-
NULL
setClassUnion("msmsWorkspaceOrNULL", "NULL")
#' Workspace for \code{msmsWorkflow} data
#'
#' A workspace which stores input and output data for \code{\link{msmsWorkflow}}.
#'
#' Slots:
#'
#' \describe{
#' \item{files}{The input file names}
#' \item{spectra}{The spectra per compound (\code{RmbSpectraSet}) extracted from the raw files}
#' \item{aggregated}{A data.frame with an aggregated peak table from all \code{spectra}.
#' Further columns are added during processing.}
#' \item{rc, rc.ms1}{The recalibration curves generated in workflow step 4.}
#' \item{parent}{For the workflow steps after 4: the parent workspace containing the state (spectra, aggregate)
#' before recalibration, such that the workflow can be reprocessed from start.}
#' \item{archivename}{The base name of the files the archive is stored to during the workflow.}
#' \item{settings}{The RMassBank settings used during the workflow, if stored with the workspace.}#'
#' }
#'
#' Methods: \describe{
#' \item{show}{Shows a brief summary of the object and processing progress.}
#' }
#'
## ' @slot files The input file names
## ' @slot spectra The spectra extracted from the raw files
## ' @slot analyzedSpecs The spectra with annotated peaks after workflow step 2.
## ' @slot aggregatedSpecs The \code{analyzedSpec} data regrouped and aggregated,
## ' after workflow step 3.
## ' @slot rc,rc.ms1 The recalibration curves generated in workflow step 4.
## ' @slot recalibratedSpecs The spectra from \code{specs} recalibrated with the curves
## ' from \code{rc, rc,ms1}.
## ' @slot analyzedRcSpecs The recalibrated spectra with annotated peaks after
## ' workflow step 5.
## ' @slot aggregatedRcSpecs The \code{analyzedRcSpec} data regrouped and aggregated,
## ' after workflow step 6.
## ' @slot reanalyzedRcSpecs The regrouped and aggregated spectra, with added reanalyzed
## ' peaks (after step 7, see \code{\link{reanalyzeFailpeaks}}).
## ' @slot refilteredRcSpecs Final data to use for MassBank record creation after
## ' multiplicity filtering (step 8).
##'
## ' @method show,msmsWorkflow Shows a brief summary of the object. Currently only the included files.
#'
#' @seealso \code{\link{msmsWorkflow}}
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @name msmsWorkspace-class
#' @docType class
#' @exportClass msmsWorkspace
#' @export
.msmsWorkspace <- setClass("msmsWorkspace",
representation = representation(
files = "character",
spectra = "RmbSpectraSetList",
aggregated = "data.frame",
parent = "msmsWorkspaceOrNULL",
rc = "ANY",
rc.ms1 = "ANY",
archivename = "character",
settings = "list"
),
contains=c("Versioned"),
prototype = prototype(
# Version 2.0.2: the RmbSpectrum2 objects were raised to version 0.1.1.
# Version 2.0.3: the RmbSpectraSet objects are now versioned and at 0.1.1.
# Version 2.0.4: polarity into Spectrum1 and RmbSpectrum2 objects
new("Versioned", versions=c(msmsWorkspace = "2.0.4")),
parent = NULL
)
)
setIs("msmsWorkspace", "msmsWorkspaceOrNULL")
#.msmsWorkspace <- setClass("msmsWorkspace",
# representation = representation(
# files = "character",
# specs = "list",
# analyzedSpecs = "list",
# aggregatedSpecs = "list",
# rc = "ANY",
# rc.ms1 = "ANY",
# recalibratedSpecs = "list",
# analyzedRcSpecs = "list",
# aggregatedRcSpecs = "list",
# reanalyzedRcSpecs = "list",
# refilteredRcSpecs = "list",
# archivename = "character",
# settings = "list"
# ),
# contains=c("Versioned"),
# prototype = prototype(
# new("Versioned", versions=c(msmsWorkspace = "1.0.1"))
# )
# )
#' Workspace for \code{mbWorkflow} data
#'
#' A workspace which stores input and output data for use with \code{mbWorkflow}.
#'
#' Slots:
#' \describe{
#' \item{spectra, aggregated}{The corresponding
#' input data from \code{\link{msmsWorkspace-class}}}
#' \item{additionalPeaks}{A list of additional peaks which can be loaded
#' using \code{\link{addPeaks}}.}
#' \item{mbdata, mbdata_archive, mbdata_relisted}{Infolist data: Data for
#' annotation of MassBank records, which can be loaded using
#' \code{\link{loadInfolists}}.}
#' \item{compiled, compiled_ok}{
#' Compiled tree-structured MassBank records. \code{compiled_ok} contains
#' only the compounds with at least one valid spectrum.}
#' \item{mbfiles}{Compiled MassBank records in text representation.}
#' \item{molfile}{MOL files with the compound structures.}
#' \item{ok,problems}{Index lists for internal use which denote which compounds
#' have valid spectra.}
#' }
#'
## ' @method show,mbWorkflow Shows a brief summary of the object. Currently only a stub.
#' Methods:
#' \describe{
#' \item{show}{Shows a brief summary of the object. Currently only a stub.}
#' }
#'
#' @seealso \code{\link{mbWorkflow}}
#'
#' @docType class
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @name mbWorkspace-class
#' @exportClass mbWorkspace
setClass("mbWorkspace",
representation(
# input data:
spectra = "RmbSpectraSetList",
aggregated = "data.frame",
## aggregatedRcSpecs = "list",
## refilteredRcSpecs = "list",
additionalPeaks = "data.frame", # ex additional_peaks
# infolists data:
mbdata = "list",
mbdata_archive = "data.frame",
mbdata_relisted = "list",
# output data:
compiled = "list",
compiled_ok = "list",
compiled_notOk = "list",
mbfiles = "list",
mbfiles_notOk = "list",
molfile = "list",
ok = "integer",
problems = "integer"
)
)
#' Create new empty workspace or load saved data for \code{msmsWorkflow}
#'
#' Creates an empty workspace or loads an existing workspace from disk.
#'
#' \code{newMsmsWorkspace} creates a new empty workspace for use with \code{msmsWorkflow.}
#'
#' \code{loadMsmsWorkspace} loads a workspace saved using \code{\link{archiveResults}}.
#' Note that it also successfully loads data saved with the old RMassBank data format
#' into the new \code{msmsWorkspace} object.
#'
#' @aliases newMsmsWorkspace loadMsmsWorkspace
#' @param files If given, the files list to initialize the workspace with.
#' @return A new \code{msmsWorkspace} object
#' @seealso \code{\link{msmsWorkflow}}, \code{\link{msmsWorkspace-class}}
#'
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @export
newMsmsWorkspace <- function(files = character(0))
{
w <- new("msmsWorkspace")
w@files <- files
return(w)
}
#' @export
loadMsmsWorkspace <- function(fileName, loadSettings = FALSE)
{
tempEnv <- new.env()
load(fileName, envir=tempEnv)
# Look if there is a msmsWorkspace in the file
objs <- ls(tempEnv)
isWs <- unlist(lapply(objs, function(obj) is(tempEnv[[obj]], "msmsWorkspace")))
whichWs <- match(TRUE, isWs)
# Found? Then just return it.
if(!is.na(whichWs))
{
w <- tempEnv[[objs[[whichWs]]]]
# If settings don't have to be loaded, erase them from the object
if(loadSettings == FALSE)
w@settings <- list()
}
# If there is no msmsWorkspace object in the workspace, this means that the workspace is version 1!
else
{
w <- new("msmsWorkspace")
dataset <- c("rc",
"rc.ms1",
"files",
"specs",
"analyzedSpecs",
"aggregatedSpecs",
"recalibratedSpecs",
"analyzedRcSpecs",
"aggregatedRcSpecs",
"reanalyzedRcSpecs",
"refilteredRcSpecs",
"archivename")
for(var in dataset)
{
if(exists(var, envir=tempEnv))
slot(w, var, check=FALSE) <- tempEnv[[var]]
classVersion(w)["msmsWorkspace"] <- "1.0.0"
}
# Check if settings exist...
if((loadSettings == TRUE) && exists("RmbSettings", envir=tempEnv))
w@settings <- tempEnv$RmbSettings
}
# process version updates
updateClass <- FALSE
if(!isVersioned(w)) updateClass <- TRUE
else if(!all(isCurrent(w))) updateClass <- TRUE
if(updateClass)
{
w <- updateObject(w)
}
# If loadSettings is set: load the settings into RMassBank
if((loadSettings == TRUE) && (length(w@settings) > 0))
loadRmbSettings(w@settings)
else if (loadSettings == TRUE)
warning("You specified to load RMassBank settings from the saved workspace, but no stored settings were found.")
return(w)
}
#' Create new workspace for \code{mbWorkflow}
#'
#' Creates a new workspace for use with \code{\link{mbWorkflow}}.
#'
#' The workspace input data will be loaded from the \code{\link{msmsWorkspace-class}}
#' object provided by the parameter \code{w}.
#'
#' @param w The input \code{msmsWorkspace} to load input data from.
#' @return A new \code{mbWorkflow} object with the loaded input data.
#'
#' @seealso \code{\link{mbWorkflow}}, \code{\link{msmsWorkspace-class}}
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @export
newMbWorkspace <- function(w)
{
#w <- fillback(w)
mb <- new("mbWorkspace",
spectra = w@spectra#,
# aggregated = w@aggregated
)
return(mb)
}
#' @name show,msmsWorkspace-method
#' @aliases show,msmsWorkspace-method
#' @param object The \code{msmsWorkspace} to display.
#' @docType methods
#' @rdname msmsWorkspace-class
#' @export
setMethod("show", "msmsWorkspace",
function(object) {
cat("Object of class \"",class(object),"\"\n",sep="")
cat(" with files: \n")
sapply(basename(object@files), function(x) cat(" -", x, "\n"))
progress <- findProgress(object)
if(4 %in% progress)
o123 <- object@parent
else
o123 <- object
## msmsWorkflow: Step 1. Get peaks ?
if(1 %in% progress){
numspecs <- 0
dummy <- sapply(o123@spectra, function(x) cat(" -", x@id, "\t foundOK:", x@found, "\n"))
cat("Peaks found:\n")
ids <- vector()
dummy1 <- sapply(o123@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) s@peaksCount), "\n")
ids <<- c(ids, x@id)
numspecs <<- numspecs + 1
return(sapply(x@children, function(s) s@peaksCount))
})
}
## msmsWorkflow: Step 2. First analysis pre recalibration
if(2 %in% progress){
cat("Peaks annotated after Step 2:\n")
dummy2 <- sapply(o123@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) length(which(s@good))), "\n")
return(sapply(x@children, function(s) length(which(s@good))))
})
whichok <- which(sapply(dummy1,length) != 0)
anyok <- whichok[1]
nrowd1 <- nrow(dummy1)
if(is.null(nrowd1))
nrowd1 <- 1
dummy2 <- matrix(unlist(dummy2), ncol = nrowd1, byrow = TRUE)
dummy1 <- matrix(unlist(dummy1), ncol = nrowd1, byrow = TRUE)
PeakMat <- dummy1-dummy2
tempids <- ids[whichok]
cat("Peaks without annotation:\n")
sapply(split(PeakMat, rep(1:nrow(PeakMat), each = ncol(PeakMat))), function(x){
cat(" -", tempids[1], "\t number of peaks filtered:", x, "\n")
tempids <<- tempids[-1]
})
}
## msmsWorkflow: Step 3. Aggregate all spectra
if(3 %in% progress){
cat("Peaks aggregated after Step 3:\n")
cat("Matched Peaks:\n")
pm <- peaksMatched(o123)
dummy <- sapply(unique(pm[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pm[,"cpdID"] == y)
peaksMatched <- pm[compoundIndex,]
uscans <- unique(peaksMatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatched[,"scan"] == z)
return(length(unique(peaksMatched[uscantemp,"mzFound"])))
}))
}), "\n"))
cat("Unmatched Peaks:\n")
pu <- peaksUnmatched(o123)
dummy <- sapply(unique(pu[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pu[,"cpdID"] == y)
peaksUnmatched <- pu[compoundIndex,]
uscans <- unique(peaksUnmatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksUnmatched[,"scan"] == z)
return(length(unique(peaksUnmatched[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra
if(4 %in% progress){
cat("Peaks successfully recalibrated:\n")
}
## msmsWorkflow: Step 5. Reanalyze recalibrated spectra
if(5 %in% progress){
cat("Peaks found after Step 5:\n")
dummy5 <- sapply(object@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) length(which(s@good))), "\n")
return(sapply(x@children, function(s) length(which(s@good))))
})
dummy5 <- matrix(unlist(dummy5), ncol = nrowd1, byrow = TRUE)
PeakMat <- dummy1-dummy5
tempids <- ids[whichok]
cat("Peaks without annotation in reanalyzed recalibrated peaks:\n")
sapply(split(PeakMat, rep(1:nrow(PeakMat), each = ncol(PeakMat))), function(x){
cat(" -", tempids[1], "\t number of peaks filtered:", x, "\n")
tempids <<- tempids[-1]
})
}
## msmsWorkflow: Step 6. Aggregate recalibrated results
if(6 %in% progress){
cat("Peaks found after Step 6:\n")
cat("Matched Peaks:\n")
pm <- peaksMatched(object)
dummy <- sapply(unique(pm[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pm[,"cpdID"] == y)
peaksMatched <- pm[compoundIndex,]
uscans <- unique(peaksMatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatched[,"scan"] == z)
return(length(unique(peaksMatched[uscantemp,"mzFound"])))
}))
}), "\n"))
cat("Unmatched Peaks:\n")
pu <- peaksUnmatched(object)
dummy <- sapply(unique(pu[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pu[,"cpdID"] == y)
peaksUnmatched <- pu[compoundIndex,]
uscans <- unique(peaksUnmatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksUnmatched[,"scan"] == z)
return(length(unique(peaksUnmatched[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 7. Reanalyze fail peaks for N2 + O
if(7 %in% progress){
cat("Peaks added in Step 7:\n")
pmr <- object@aggregated[object@aggregated$matchedReanalysis & !is.na(object@aggregated$matchedReanalysis),,drop=FALSE]
dummy <- sapply(unique(pmr[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pmr[,"cpdID"] == y)
peaksMatchedReanalysis <- pmr[compoundIndex,]
uscans <- unique(peaksMatchedReanalysis[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatchedReanalysis[,"scan"] == z)
return(length(unique(peaksMatchedReanalysis[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 8. Peak multiplicity filtering
if(8 %in% progress){
cat("After Step 8: multiplicity filtering:\n")
show(table(object@aggregated[object@aggregated$filterOK & is.na(object@aggregated$matchedReanalysis), "cpdID"]))
show(table(object@aggregated[object@aggregated$filterOK & !is.na(object@aggregated$matchedReanalysis), "cpdID"]))
}
})
#'
#' @name show,mbWorkspace-method
#' @aliases show,mbWorkspace-method
#' @rdname mbWorkspace-class
#' @param object The \code{mbWorkspace} to display.
#' @docType methods
#' @export
setMethod("show", "mbWorkspace",
function(object) {
cat("Object of class \"",class(object),"\"\n",sep="")
str(object, max.level=2)
})
#' Plots mbWorkspaces
#'
#' Plots the peaks of one or two \code{mbWorkspace} to compare them.
#'
#' This functions plots one or two \code{mbWorkspace}s in case the use has used different methods to acquire
#' similar spectra. \code{w1} must always be supplied, while \code{w2} is optional. The wokspaces need to be fully processed
#' for this function to work.
#'
#' @param w1 The \code{mbWorkspace} to be plotted
#' @param w2 Another optional \code{mbWorkspace} be plotted as a reference.
#' @return A logical indicating whether the information was plotted or not
#' @author Erik Mueller
#' @examples
#'
#' #
#' \dontrun{plotMbWorkspaces(w1,w2)}
#'
#' @export
plotMbWorkspaces <- function(w1, w2=NULL){
if(class(w1) != "mbWorkspace"){
stop("The first supplied argument must be an mbWorkspace")
}
if(!is.null(w2)){
if(class(w2) != "mbWorkspace"){
stop("If there is a second argument supplied it must be of class mbWorkspace")
}
pl2 <- lapply(w2@compiled_ok,function(x){
lapply(x,function(y) y[['PK$PEAK']][,c("m/z","rel.int.")])
})
plot_title <- lapply(w2@compiled_ok,function(x){
lapply(x,function(y) y[['ACCESSION']])
})
}
pl1 <- lapply(w1@compiled_ok,function(x){
lapply(x,function(y) y[['PK$PEAK']][,c("m/z","rel.int.")])
})
plot_title <- lapply(w1@compiled_ok,function(x){
lapply(x,function(y) y[['ACCESSION']])
})
maxpeaks <- c(-1000,1000)
if(length(pl1) != 0){
for(i in 1:length(pl1)){
currentCompound <- pl1[[i]]
for(j in 1:length(currentCompound)){
currentSpectrum <- currentCompound[[j]]
plot(currentSpectrum[,"m/z"],currentSpectrum[,"rel.int."], type="h",col="green",lwd=3,ylim=maxpeaks,xlab = "mz", ylab="rel.int", main=plot_title[[i]][[j]])
if(!is.null(w2)){
points(pl2[[i]][[j]][,"m/z"],-pl2[[i]][[j]][,"rel.int."], type="h",col="red",lwd=3)
}
}
}
}
return(TRUE)
}
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Defines functions plotMbWorkspaces newMbWorkspace loadMsmsWorkspace newMsmsWorkspace
Documented in loadMsmsWorkspace newMbWorkspace newMsmsWorkspace plotMbWorkspaces
#' @import methods
#' @importFrom Biobase isVersioned
#' @importFrom Biobase isCurrent
#' @importFrom Biobase classVersion<-
NULL
setClassUnion("msmsWorkspaceOrNULL", "NULL")
#' Workspace for \code{msmsWorkflow} data
#'
#' A workspace which stores input and output data for \code{\link{msmsWorkflow}}.
#'
#' Slots:
#'
#' \describe{
#' \item{files}{The input file names}
#' \item{spectra}{The spectra per compound (\code{RmbSpectraSet}) extracted from the raw files}
#' \item{aggregated}{A data.frame with an aggregated peak table from all \code{spectra}.
#' Further columns are added during processing.}
#' \item{rc, rc.ms1}{The recalibration curves generated in workflow step 4.}
#' \item{parent}{For the workflow steps after 4: the parent workspace containing the state (spectra, aggregate)
#' before recalibration, such that the workflow can be reprocessed from start.}
#' \item{archivename}{The base name of the files the archive is stored to during the workflow.}
#' \item{settings}{The RMassBank settings used during the workflow, if stored with the workspace.}#'
#' }
#'
#' Methods: \describe{
#' \item{show}{Shows a brief summary of the object and processing progress.}
#' }
#'
## ' @slot files The input file names
## ' @slot spectra The spectra extracted from the raw files
## ' @slot analyzedSpecs The spectra with annotated peaks after workflow step 2.
## ' @slot aggregatedSpecs The \code{analyzedSpec} data regrouped and aggregated,
## ' after workflow step 3.
## ' @slot rc,rc.ms1 The recalibration curves generated in workflow step 4.
## ' @slot recalibratedSpecs The spectra from \code{specs} recalibrated with the curves
## ' from \code{rc, rc,ms1}.
## ' @slot analyzedRcSpecs The recalibrated spectra with annotated peaks after
## ' workflow step 5.
## ' @slot aggregatedRcSpecs The \code{analyzedRcSpec} data regrouped and aggregated,
## ' after workflow step 6.
## ' @slot reanalyzedRcSpecs The regrouped and aggregated spectra, with added reanalyzed
## ' peaks (after step 7, see \code{\link{reanalyzeFailpeaks}}).
## ' @slot refilteredRcSpecs Final data to use for MassBank record creation after
## ' multiplicity filtering (step 8).
##'
## ' @method show,msmsWorkflow Shows a brief summary of the object. Currently only the included files.
#'
#' @seealso \code{\link{msmsWorkflow}}
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @name msmsWorkspace-class
#' @docType class
#' @exportClass msmsWorkspace
#' @export
.msmsWorkspace <- setClass("msmsWorkspace",
representation = representation(
files = "character",
spectra = "RmbSpectraSetList",
aggregated = "data.frame",
parent = "msmsWorkspaceOrNULL",
rc = "ANY",
rc.ms1 = "ANY",
archivename = "character",
settings = "list"
),
contains=c("Versioned"),
prototype = prototype(
# Version 2.0.2: the RmbSpectrum2 objects were raised to version 0.1.1.
# Version 2.0.3: the RmbSpectraSet objects are now versioned and at 0.1.1.
# Version 2.0.4: polarity into Spectrum1 and RmbSpectrum2 objects
new("Versioned", versions=c(msmsWorkspace = "2.0.4")),
parent = NULL
)
)
setIs("msmsWorkspace", "msmsWorkspaceOrNULL")
#.msmsWorkspace <- setClass("msmsWorkspace",
# representation = representation(
# files = "character",
# specs = "list",
# analyzedSpecs = "list",
# aggregatedSpecs = "list",
# rc = "ANY",
# rc.ms1 = "ANY",
# recalibratedSpecs = "list",
# analyzedRcSpecs = "list",
# aggregatedRcSpecs = "list",
# reanalyzedRcSpecs = "list",
# refilteredRcSpecs = "list",
# archivename = "character",
# settings = "list"
# ),
# contains=c("Versioned"),
# prototype = prototype(
# new("Versioned", versions=c(msmsWorkspace = "1.0.1"))
# )
# )
#' Workspace for \code{mbWorkflow} data
#'
#' A workspace which stores input and output data for use with \code{mbWorkflow}.
#'
#' Slots:
#' \describe{
#' \item{spectra, aggregated}{The corresponding
#' input data from \code{\link{msmsWorkspace-class}}}
#' \item{additionalPeaks}{A list of additional peaks which can be loaded
#' using \code{\link{addPeaks}}.}
#' \item{mbdata, mbdata_archive, mbdata_relisted}{Infolist data: Data for
#' annotation of MassBank records, which can be loaded using
#' \code{\link{loadInfolists}}.}
#' \item{compiled, compiled_ok}{
#' Compiled tree-structured MassBank records. \code{compiled_ok} contains
#' only the compounds with at least one valid spectrum.}
#' \item{mbfiles}{Compiled MassBank records in text representation.}
#' \item{molfile}{MOL files with the compound structures.}
#' \item{ok,problems}{Index lists for internal use which denote which compounds
#' have valid spectra.}
#' }
#'
## ' @method show,mbWorkflow Shows a brief summary of the object. Currently only a stub.
#' Methods:
#' \describe{
#' \item{show}{Shows a brief summary of the object. Currently only a stub.}
#' }
#'
#' @seealso \code{\link{mbWorkflow}}
#'
#' @docType class
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @name mbWorkspace-class
#' @exportClass mbWorkspace
setClass("mbWorkspace",
representation(
# input data:
spectra = "RmbSpectraSetList",
aggregated = "data.frame",
## aggregatedRcSpecs = "list",
## refilteredRcSpecs = "list",
additionalPeaks = "data.frame", # ex additional_peaks
# infolists data:
mbdata = "list",
mbdata_archive = "data.frame",
mbdata_relisted = "list",
# output data:
compiled = "list",
compiled_ok = "list",
compiled_notOk = "list",
mbfiles = "list",
mbfiles_notOk = "list",
molfile = "list",
ok = "integer",
problems = "integer"
)
)
#' Create new empty workspace or load saved data for \code{msmsWorkflow}
#'
#' Creates an empty workspace or loads an existing workspace from disk.
#'
#' \code{newMsmsWorkspace} creates a new empty workspace for use with \code{msmsWorkflow.}
#'
#' \code{loadMsmsWorkspace} loads a workspace saved using \code{\link{archiveResults}}.
#' Note that it also successfully loads data saved with the old RMassBank data format
#' into the new \code{msmsWorkspace} object.
#'
#' @aliases newMsmsWorkspace loadMsmsWorkspace
#' @param files If given, the files list to initialize the workspace with.
#' @return A new \code{msmsWorkspace} object
#' @seealso \code{\link{msmsWorkflow}}, \code{\link{msmsWorkspace-class}}
#'
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @export
newMsmsWorkspace <- function(files = character(0))
{
w <- new("msmsWorkspace")
w@files <- files
return(w)
}
#' @export
loadMsmsWorkspace <- function(fileName, loadSettings = FALSE)
{
tempEnv <- new.env()
load(fileName, envir=tempEnv)
# Look if there is a msmsWorkspace in the file
objs <- ls(tempEnv)
isWs <- unlist(lapply(objs, function(obj) is(tempEnv[[obj]], "msmsWorkspace")))
whichWs <- match(TRUE, isWs)
# Found? Then just return it.
if(!is.na(whichWs))
{
w <- tempEnv[[objs[[whichWs]]]]
# If settings don't have to be loaded, erase them from the object
if(loadSettings == FALSE)
w@settings <- list()
}
# If there is no msmsWorkspace object in the workspace, this means that the workspace is version 1!
else
{
w <- new("msmsWorkspace")
dataset <- c("rc",
"rc.ms1",
"files",
"specs",
"analyzedSpecs",
"aggregatedSpecs",
"recalibratedSpecs",
"analyzedRcSpecs",
"aggregatedRcSpecs",
"reanalyzedRcSpecs",
"refilteredRcSpecs",
"archivename")
for(var in dataset)
{
if(exists(var, envir=tempEnv))
slot(w, var, check=FALSE) <- tempEnv[[var]]
classVersion(w)["msmsWorkspace"] <- "1.0.0"
}
# Check if settings exist...
if((loadSettings == TRUE) && exists("RmbSettings", envir=tempEnv))
w@settings <- tempEnv$RmbSettings
}
# process version updates
updateClass <- FALSE
if(!isVersioned(w)) updateClass <- TRUE
else if(!all(isCurrent(w))) updateClass <- TRUE
if(updateClass)
{
w <- updateObject(w)
}
# If loadSettings is set: load the settings into RMassBank
if((loadSettings == TRUE) && (length(w@settings) > 0))
loadRmbSettings(w@settings)
else if (loadSettings == TRUE)
warning("You specified to load RMassBank settings from the saved workspace, but no stored settings were found.")
return(w)
}
#' Create new workspace for \code{mbWorkflow}
#'
#' Creates a new workspace for use with \code{\link{mbWorkflow}}.
#'
#' The workspace input data will be loaded from the \code{\link{msmsWorkspace-class}}
#' object provided by the parameter \code{w}.
#'
#' @param w The input \code{msmsWorkspace} to load input data from.
#' @return A new \code{mbWorkflow} object with the loaded input data.
#'
#' @seealso \code{\link{mbWorkflow}}, \code{\link{msmsWorkspace-class}}
#' @author Michael Stravs, Eawag <michael.stravs@@eawag.ch>
#' @export
newMbWorkspace <- function(w)
{
#w <- fillback(w)
mb <- new("mbWorkspace",
spectra = w@spectra#,
# aggregated = w@aggregated
)
return(mb)
}
#' @name show,msmsWorkspace-method
#' @aliases show,msmsWorkspace-method
#' @param object The \code{msmsWorkspace} to display.
#' @docType methods
#' @rdname msmsWorkspace-class
#' @export
setMethod("show", "msmsWorkspace",
function(object) {
cat("Object of class \"",class(object),"\"\n",sep="")
cat(" with files: \n")
sapply(basename(object@files), function(x) cat(" -", x, "\n"))
progress <- findProgress(object)
if(4 %in% progress)
o123 <- object@parent
else
o123 <- object
## msmsWorkflow: Step 1. Get peaks ?
if(1 %in% progress){
numspecs <- 0
dummy <- sapply(o123@spectra, function(x) cat(" -", x@id, "\t foundOK:", x@found, "\n"))
cat("Peaks found:\n")
ids <- vector()
dummy1 <- sapply(o123@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) s@peaksCount), "\n")
ids <<- c(ids, x@id)
numspecs <<- numspecs + 1
return(sapply(x@children, function(s) s@peaksCount))
})
}
## msmsWorkflow: Step 2. First analysis pre recalibration
if(2 %in% progress){
cat("Peaks annotated after Step 2:\n")
dummy2 <- sapply(o123@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) length(which(s@good))), "\n")
return(sapply(x@children, function(s) length(which(s@good))))
})
whichok <- which(sapply(dummy1,length) != 0)
anyok <- whichok[1]
nrowd1 <- nrow(dummy1)
if(is.null(nrowd1))
nrowd1 <- 1
dummy2 <- matrix(unlist(dummy2), ncol = nrowd1, byrow = TRUE)
dummy1 <- matrix(unlist(dummy1), ncol = nrowd1, byrow = TRUE)
PeakMat <- dummy1-dummy2
tempids <- ids[whichok]
cat("Peaks without annotation:\n")
sapply(split(PeakMat, rep(1:nrow(PeakMat), each = ncol(PeakMat))), function(x){
cat(" -", tempids[1], "\t number of peaks filtered:", x, "\n")
tempids <<- tempids[-1]
})
}
## msmsWorkflow: Step 3. Aggregate all spectra
if(3 %in% progress){
cat("Peaks aggregated after Step 3:\n")
cat("Matched Peaks:\n")
pm <- peaksMatched(o123)
dummy <- sapply(unique(pm[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pm[,"cpdID"] == y)
peaksMatched <- pm[compoundIndex,]
uscans <- unique(peaksMatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatched[,"scan"] == z)
return(length(unique(peaksMatched[uscantemp,"mzFound"])))
}))
}), "\n"))
cat("Unmatched Peaks:\n")
pu <- peaksUnmatched(o123)
dummy <- sapply(unique(pu[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pu[,"cpdID"] == y)
peaksUnmatched <- pu[compoundIndex,]
uscans <- unique(peaksUnmatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksUnmatched[,"scan"] == z)
return(length(unique(peaksUnmatched[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra
if(4 %in% progress){
cat("Peaks successfully recalibrated:\n")
}
## msmsWorkflow: Step 5. Reanalyze recalibrated spectra
if(5 %in% progress){
cat("Peaks found after Step 5:\n")
dummy5 <- sapply(object@spectra, function(x) {
cat(" -", x@id, "\t peaks:",
sapply(x@children, function(s) length(which(s@good))), "\n")
return(sapply(x@children, function(s) length(which(s@good))))
})
dummy5 <- matrix(unlist(dummy5), ncol = nrowd1, byrow = TRUE)
PeakMat <- dummy1-dummy5
tempids <- ids[whichok]
cat("Peaks without annotation in reanalyzed recalibrated peaks:\n")
sapply(split(PeakMat, rep(1:nrow(PeakMat), each = ncol(PeakMat))), function(x){
cat(" -", tempids[1], "\t number of peaks filtered:", x, "\n")
tempids <<- tempids[-1]
})
}
## msmsWorkflow: Step 6. Aggregate recalibrated results
if(6 %in% progress){
cat("Peaks found after Step 6:\n")
cat("Matched Peaks:\n")
pm <- peaksMatched(object)
dummy <- sapply(unique(pm[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pm[,"cpdID"] == y)
peaksMatched <- pm[compoundIndex,]
uscans <- unique(peaksMatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatched[,"scan"] == z)
return(length(unique(peaksMatched[uscantemp,"mzFound"])))
}))
}), "\n"))
cat("Unmatched Peaks:\n")
pu <- peaksUnmatched(object)
dummy <- sapply(unique(pu[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pu[,"cpdID"] == y)
peaksUnmatched <- pu[compoundIndex,]
uscans <- unique(peaksUnmatched[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksUnmatched[,"scan"] == z)
return(length(unique(peaksUnmatched[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 7. Reanalyze fail peaks for N2 + O
if(7 %in% progress){
cat("Peaks added in Step 7:\n")
pmr <- object@aggregated[object@aggregated$matchedReanalysis & !is.na(object@aggregated$matchedReanalysis),,drop=FALSE]
dummy <- sapply(unique(pmr[,"cpdID"]), function(x) cat(" -", x, "\t peaks:",
sapply(x, function(y){
compoundIndex <- which(pmr[,"cpdID"] == y)
peaksMatchedReanalysis <- pmr[compoundIndex,]
uscans <- unique(peaksMatchedReanalysis[,"scan"])
return(sapply(uscans, function(z){
uscantemp <- which(peaksMatchedReanalysis[,"scan"] == z)
return(length(unique(peaksMatchedReanalysis[uscantemp,"mzFound"])))
}))
}), "\n"))
}
## msmsWorkflow: Step 8. Peak multiplicity filtering
if(8 %in% progress){
cat("After Step 8: multiplicity filtering:\n")
show(table(object@aggregated[object@aggregated$filterOK & is.na(object@aggregated$matchedReanalysis), "cpdID"]))
show(table(object@aggregated[object@aggregated$filterOK & !is.na(object@aggregated$matchedReanalysis), "cpdID"]))
}
})
#'
#' @name show,mbWorkspace-method
#' @aliases show,mbWorkspace-method
#' @rdname mbWorkspace-class
#' @param object The \code{mbWorkspace} to display.
#' @docType methods
#' @export
setMethod("show", "mbWorkspace",
function(object) {
cat("Object of class \"",class(object),"\"\n",sep="")
str(object, max.level=2)
})
#' Plots mbWorkspaces
#'
#' Plots the peaks of one or two \code{mbWorkspace} to compare them.
#'
#' This functions plots one or two \code{mbWorkspace}s in case the use has used different methods to acquire
#' similar spectra. \code{w1} must always be supplied, while \code{w2} is optional. The wokspaces need to be fully processed
#' for this function to work.
#'
#' @param w1 The \code{mbWorkspace} to be plotted
#' @param w2 Another optional \code{mbWorkspace} be plotted as a reference.
#' @return A logical indicating whether the information was plotted or not
#' @author Erik Mueller
#' @examples
#'
#' #
#' \dontrun{plotMbWorkspaces(w1,w2)}
#'
#' @export
plotMbWorkspaces <- function(w1, w2=NULL){
if(class(w1) != "mbWorkspace"){
stop("The first supplied argument must be an mbWorkspace")
}
if(!is.null(w2)){
if(class(w2) != "mbWorkspace"){
stop("If there is a second argument supplied it must be of class mbWorkspace")
}
pl2 <- lapply(w2@compiled_ok,function(x){
lapply(x,function(y) y[['PK$PEAK']][,c("m/z","rel.int.")])
})
plot_title <- lapply(w2@compiled_ok,function(x){
lapply(x,function(y) y[['ACCESSION']])
})
}
pl1 <- lapply(w1@compiled_ok,function(x){
lapply(x,function(y) y[['PK$PEAK']][,c("m/z","rel.int.")])
})
plot_title <- lapply(w1@compiled_ok,function(x){
lapply(x,function(y) y[['ACCESSION']])
})
maxpeaks <- c(-1000,1000)
if(length(pl1) != 0){
for(i in 1:length(pl1)){
currentCompound <- pl1[[i]]
for(j in 1:length(currentCompound)){
currentSpectrum <- currentCompound[[j]]
plot(currentSpectrum[,"m/z"],currentSpectrum[,"rel.int."], type="h",col="green",lwd=3,ylim=maxpeaks,xlab = "mz", ylab="rel.int", main=plot_title[[i]][[j]])
if(!is.null(w2)){
points(pl2[[i]][[j]][,"m/z"],-pl2[[i]][[j]][,"rel.int."], type="h",col="red",lwd=3)
}
}
}
}
return(TRUE)
}
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