Nothing
R/DataClasses.R
In MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics
Defines functions
getClassVersionString
getClassVersion
## Returns of class version as documented in .MSnBaseEnd$ClassVersions
## as and instance of class Versions.
getClassVersion <- function(x) {
if (!is.character(x))
x <- class(x)[1]
## This get class versions from parent classes (if any)
ver <- classVersion(x)
## Adds (or overwrites) x's class version to the list of class
## versions
ver[x] <- getClassVersionString(x)
ver
}
## Utility to just extract the version string from the environment.
getClassVersionString <- function(x) {
if (!is.character(x))
x <- class(x)[1]
return(.MSnbaseEnv$ClassVersions[x])
}
######################################################################
## MSnProcess: Container for MSnExp and MSnSet processing information
## See online documentation for more information.
setClass("MSnProcess",
representation = representation(
files = "character",
processing = "character",
merged = "logical",
cleaned = "logical",
removedPeaks = "character",
smoothed = "logical",
trimmed = "numeric",
normalised = "logical",
MSnbaseVersion = "character"),
contains = c("Versioned"),
prototype = prototype(
new("Versioned", versions = c(MSnProcess = "0.1.3")),
processing = character(),
files = character(),
trimmed = numeric(),
removedPeaks = character(),
MSnbaseVersion = character())) ## set in initialize()
#################################################################
## The 'Minimum Information About a Proteomics Experiment' Class
## See online documentation for more information.
setClass("MIAPE",
representation = representation(
title = "character",
url = "character",
## Publication details
abstract = "character",
pubMedIds = "character",
## Other useful slots, from MIAME
samples = "list",
preprocessing = "list",
other = "list",
## ########################
## Based on MIAPE-MS 2.24
## will be updated with MIAPE-MSI and MIAPE-Quant
## 1. General features - (a) Global descriptors
dateStamp = "character",
## Responsible person
name = "character",
lab = "character",
contact = "character",
email = "character",
## Instrument details
instrumentModel = "character",
instrumentManufacturer = "character",
instrumentCustomisations = "character",
## 1. General features - (b) Control and analysis software
softwareName = "character",
softwareVersion = "character",
switchingCriteria = "character",
isolationWidth = "numeric",
parameterFile = "character",
## 2. Ion sources -- will be updated to
## provided details specific to
## different sources
ionSource = "character", ## ESI, MALDI, ...
ionSourceDetails = "character",
## 3. Post-source component
analyser = "character", ## Quad, TOF, Trap, ...
analyserDetails = "character",
## 3. Post-source component - (d) Collision cell
collisionGas = "character",
collisionPressure = "numeric",
collisionEnergy = "character",
## 3. Post-source component - (f) Detectors
detectorType = "character",
detectorSensitivity = "character"
## 4. Spectrum and peak list generation and annotation
## (a) Spectrum description
## (b) Peak list generation
## (c) Quantitation for selected ions
## -- see Spectrum and MSnProcess objects
),
contains = c("MIAxE"),
prototype = prototype(
new("Versioned",
versions = c(classVersion("MIAxE"), MIAPE = "0.2.2")),
name = "",
lab = "",
contact = "",
title = "",
abstract = "",
url = "",
pubMedIds = "",
email = "",
samples = list(),
preprocessing = list(),
other = list()))
#############################################################################
## pSet: similarly to eSet but with a focus toward proteomics experiments,
## pSet is a VIRTUAL class containing assay data (typically, one or many
## different sets of spectra), phenotypic data (describing the samples involved
## in the experiment), experimental data (describing the methods and
## protocols used) and feature data (describing the features in the assay).
##
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
setClass("pSet",
representation(assayData = "environment", ## locked environment
phenoData = "AnnotatedDataFrame",
featureData = "AnnotatedDataFrame",
# How to link individual spectra in assayData env to featureData?
# The spectra and the rowNames of featureData will be named X1..(n1+n2+...)
experimentData = "MIAxE",
protocolData = "AnnotatedDataFrame",
processingData = "MSnProcess",
##onDisk = "logical",
.cache = "environment", ## locked
"VIRTUAL"),
contains = "Versioned",
prototype = prototype(
new("Versioned", versions = c(pSet = "0.1.1")),
assayData = new.env(parent=emptyenv()),
experimentData = new("MIAPE"),
phenoData = new("AnnotatedDataFrame",
dimLabels=c("sampleNames", "fileNumbers")),
featureData = new("AnnotatedDataFrame",
dimLabels=c("featureNames", "featureColumns")),
protocolData = new("AnnotatedDataFrame",
dimLabels=c("sampleNames", "sampleColumns"))
##,onDisk = FALSE
)
)
##################################################################
## Container for MSn Experiments Data and Meta-Data
## See online documentation for more information.
setClass("MSnExp",
contains=c("pSet"),
prototype = prototype(
new("VersionedBiobase",
versions = c(classVersion("pSet"), MSnExp="0.3.1")),
experimentData = new("MIAPE"))
)
###################################################################
## The Spectrum class and it's sub-classes Spectrum1 and Spectrum2
## See online documentation for more information.
setClass("Spectrum",
representation = representation(
msLevel="integer",
peaksCount="integer",
rt="numeric",
acquisitionNum="integer",
scanIndex = "integer",
tic = "numeric",
mz = "numeric",
intensity = "numeric",
fromFile = "integer",
centroided = "logical",
smoothed = "logical",
polarity="integer",
"VIRTUAL"),
contains=c("Versioned"),
prototype = prototype(
rt = numeric(),
polarity = NA_integer_,
acquisitionNum = NA_integer_,
msLevel = NA_integer_,
centroided = NA,
smoothed = NA,
peaksCount = 0L,
tic = 0,
scanIndex = integer(),
mz = numeric(),
intensity = numeric()),
validity = function(object)
validSpectrum(object)
)
setClass("Spectrum2",
representation = representation(
merged="numeric",
precScanNum="integer",
precursorMz="numeric",
precursorIntensity = "numeric",
precursorCharge = "integer",
collisionEnergy = "numeric"),
contains=c("Spectrum"),
prototype = prototype(
merged = 1,
acquisitionNum = integer(),
precScanNum = integer(),
precursorMz = numeric(),
precursorIntensity = numeric(),
msLevel = as.integer(2),
precursorCharge = integer(),
collisionEnergy = numeric()),
validity = function(object) {
msg <- validMsg(NULL, NULL)
msl <- object@msLevel
if (msl < as.integer(2))
msg <- validMsg(msg,
paste0("Object of class ",
class(object),
" but msLevel is ", msl,
" (should be > 1)"))
if (is.null(msg)) TRUE
else msg
})
setClass("Spectrum1",
contains=c("Spectrum"),
prototype = prototype(
polarity=integer(),
msLevel = as.integer(1)),
validity = function(object) {
msg <- validMsg(NULL, NULL)
msl <- object@msLevel
if (msl!=as.integer(1))
msg <- validMsg(msg,paste("Object of class",class(object),
"but msLevel is",msl,sep=" "))
if (is.null(msg)) TRUE
else msg
})
##################################################################
## Data Structure for Reporter Ions for labelled MS Quantification
## See online documentation for more information.
setClass("ReporterIons",
representation = representation(
name = "character",
reporterNames = "character",
description = "character",
mz = "numeric",
col = "character",
width = "numeric"),
contains = c("Versioned"),
prototype = prototype(
new("Versioned", versions = c(ReporterIons = "0.1.0")),
name = character(),
reporterNames = character(),
description = character(),
mz = numeric(),
col = character(),
width = numeric()),
validity = function(object) {
msg <- validMsg(NULL, NULL)
if (length(object@mz) == 0) {
msg <- validMsg(msg, "No reporter ions defined.")
} else {
if (length(object@col) != length(object@mz))
warning("Missing color(s) for the reporter ions.")
}
if (is.null(msg)) TRUE
else msg
})
#####################################################################
## The "MSnSet" Class for MS Proteomics Expression Data and Meta-Data
## See online documentation for more information.
setClass("MSnSet",
representation = representation(
experimentData="MIAPE",
processingData = "MSnProcess",
qual = "data.frame"),
contains = "eSet",
prototype = prototype(
new("VersionedBiobase",
versions=c(classVersion("eSet"),
classVersion("pSet"),
MSnSet="0.4.0")),
experimentData=new("MIAPE"),
annotation="No feature annotation."))
.MSnSetList <-
setClass("MSnSetList",
slots = c(x = "list",
log = "list",
featureData = "DataFrame"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(MSnSetList = "0.2.0"))),
validity = function(object) {
msg <- validMsg(NULL, NULL)
if (!listOf(object@x, "MSnSet", valid = FALSE))
msg <- validMsg(msg, "Not all items are MSnSets.")
nvals <- sapply(object@x, validObject)
if (!all(nvals))
msg <- validMsg(msg,
paste(sum(!nvals),
"MSnSets are not valid."))
if (length(object@x) != nrow(object@featureData))
msg <- validMsg(msg,
"Data and meta-data dimensions don't match.")
if (length(object@x) &&
!identical(names(object@x), rownames(object@featureData)))
msg <- validMsg(msg,
"Data and meta-data names don't match.")
if (is.null(msg)) TRUE
else msg
})
#####################################################################
## Features of interest infrastructure
.FeaturesOfInterest <-
setClass("FeaturesOfInterest",
slots = c(
description = "character",
fnames = "character",
date = "character",
objpar = "list"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(FeaturesOfInterest = "0.1.0"))))
.FoICollection <-
setClass("FoICollection",
slots = c(foic = "list"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(FeaturesOfInterest = "0.1.0"))))
#####################################################################
## Support for the PSI mzTab format
.MzTab <- setClass("MzTab",
slots = c(
Metadata = "list",
Filename = "character",
Proteins = "data.frame",
Peptides = "data.frame",
PSMs = "data.frame",
SmallMolecules = "data.frame",
MoleculeFeatures = "data.frame",
MoleculeEvidence = "data.frame",
Comments = "character"))
##################################################################
## Container for MSn Experiments Data and Meta-Data enabling/allowing
## to process raw MS files on-the-fly (without the need to keep all
## data in memory).
setClass("OnDiskMSnExp",
representation=representation(
spectraProcessingQueue="list", ## List collecting ProcessingSteps for lazy processing.
backend="character" ## That's to eventually add a SQLite backend later...
),
contains=c("MSnExp"),
prototype = prototype(
new("VersionedBiobase",
versions = c(classVersion("MSnExp"), OnDiskMSnExp="0.0.1")),
spectraProcessingQueue=list(),
backend=character()),
validity=function(object){
## Return true if the object is empty.
if (length(object) == 0)
return(TRUE)
## Ensure that the files (returned by fileNames) are available
## and check also that the featureData contains all the required
## information.
msg <- validMsg(NULL, NULL)
## Elements in spectraProcessingQueue have to be ProcessingStep objects.
if (length(object@spectraProcessingQueue) > 0){
isOK <- unlist(lapply(object@spectraProcessingQueue,
function(z)
return(is(z, "ProcessingStep"))))
if (any(!isOK))
msg <- validMsg(msg,
paste0("Only objects of type 'ProcessingStep'",
" allowed in slot 'spectraProcessingQueue'"))
}
## Check that required columns are present in the featureData:
msg <- validMsg(msg, validateFeatureDataForOnDiskMSnExp(featureData(object)))
## Check if the files do exist.
theFiles <- fileNames(object)
for (theF in theFiles){
if (!file.exists(theF))
msg <- validMsg(msg,
paste0("Required data file '", basename(theF),
"' not found!"))
}
## Some last checks I had to take from the pSet as validObject on OnDiskMSnExp was first
## calling the pSet validate method on the MSnExp and that caused a problem since we don't
## have an assayData with spectra here (fromFile was trying to get that form there).
aFileIds <- fromFile(object)
fFileIds <- fData(object)$fileIdx
if (length(fFileIds) && any(aFileIds != fFileIds))
msg <- validMsg(msg, "Mismatch of files in assayData and processingData.")
## Check if the fromFile values match to @files in processingData
filesProcData <- 1:length(processingData(object)@files)
if ( !all(unique(sort(aFileIds)) == unique(sort(filesProcData))) )
msg <- validMsg(msg, "Spectra file indices in assayData does not match files in processinData.")
nfilesprocData <- length(processingData(object)@files)
nfilesSpectra <- length(unique(aFileIds))
if (nfilesprocData < nfilesSpectra)
msg <- validMsg(msg, "More spectra files in assayData than in processingData.")
if (length(sampleNames(object)) != nrow(pData(object)))
msg <- validMsg(msg, "Different number of samples accoring to sampleNames and pData.")
## Check also experimentData:
if (length(
unique(c(length(fileNames(object)),
length(experimentData(object)@instrumentManufacturer),
length(experimentData(object)@instrumentModel),
length(experimentData(object)@ionSource),
length(experimentData(object)@analyser),
length(experimentData(object)@detectorType)))) != 1)
msg <- validMsg(msg, "The number of files does not match the information in experimentData.")
if (!isOnDisk(object))
msg <- validMsg(msg, "Object is not 'onDisk'.")
if (!isEmpty(object@assayData))
msg <- validMsg(msg, "Assaydata is not empty.")
if (is.null(msg)) {
return(TRUE)
} else {
return(msg)
}
})
############################################################
## Simple class defining a "processing" step, consisting of a
## function name (@FUN) and optional arguments (@ARG)
setClass("ProcessingStep",
representation=representation(
FUN="character",
ARGS="list"
),
contains="Versioned",
prototype=prototype(
new("Versioned",
versions=c(ProcessingStep="0.0.1")),
ARGS=list(),
FUN=character()
),
validity=function(object){
msg <- validMsg(NULL, NULL)
## Check if function/method exists?
if(length(object@FUN) > 0){
Res <- try(get(object@FUN), silent=TRUE)
if(is(Res, "try-error")){
msg <- validMsg(msg,
paste0("Function '", object@FUN, "' not found!"))
}else{
if(!is(Res, "function"))
msg <- validMsg(msg,
paste0("'", object@FUN, "' is not a function!"))
}
}
if(is.null(msg)){
return(TRUE)
}else{
return(msg)
}
})
#' @title Representation of chromatographic MS data
#'
#' @description The \code{Chromatogram} class is designed to store
#' chromatographic MS data, i.e. pairs of retention time and intensity
#' values. Instances of the class can be created with the
#' \code{Chromatogram} constructor function but in most cases the dedicated
#' methods for \code{\linkS4class{OnDiskMSnExp}} and
#' \code{\linkS4class{MSnExp}} objects extracting chromatograms should be
#' used instead (i.e. the \code{\link{chromatogram}} method).
#'
#' @details The \code{mz}, \code{filterMz}, \code{precursorMz} and
#' \code{productMz} are stored as a \code{numeric(2)} representing a range
#' even if the chromatogram was generated for only a single ion (i.e. a
#' single mz value). Using ranges for \code{mz} values allow this class to
#' be used also for e.g. total ion chromatograms or base peak chromatograms.
#'
#' The slots \code{precursorMz} and \code{productMz} allow to represent SRM
#' (single reaction monitoring) and MRM (multiple SRM) chromatograms. As
#' example, a \code{Chromatogram} for a SRM transition 273 -> 153 will have
#' a \code{@precursorMz = c(273, 273)} and a
#' \code{@productMz = c(153, 153)}.
#'
#' @rdname Chromatogram-class
#'
#' @export
#'
#' @seealso \code{\link{MChromatograms}} for combining \code{Chromatogram} in
#' a two-dimensional matrix (rows being mz-rt ranges, columns samples).
#' \code{\link{chromatogram}} for the method to extract chromatogram data
#' from a \code{\linkS4class{MSnExp}} or \code{\linkS4class{OnDiskMSnExp}}
#' object.
#' \code{\link{clean}} for the method to \emph{clean} a \code{Chromatogram}
#' object.
#'
#' @author Johannes Rainer
#'
#' @examples
#'
#' ## Create a simple Chromatogram object.
#' ints <- abs(rnorm(100, sd = 100))
#' rts <- seq_len(length(ints))
#' chr <- Chromatogram(rtime = rts, intensity = ints)
#' chr
#'
#' ## Extract intensities
#' intensity(chr)
#'
#' ## Extract retention times
#' rtime(chr)
#'
#' ## Extract the mz range - is NA for the present example
#' mz(chr)
#'
#' ## plot the Chromatogram
#' plot(chr)
setClass("Chromatogram",
slots = c(
rtime = "numeric",
intensity = "numeric",
mz = "numeric",
filterMz = "numeric",
precursorMz = "numeric", ## Or call that Q1mz?
productMz = "numeric", ## Or call that Q3mz?
fromFile = "integer",
aggregationFun = "character",
msLevel = "integer"
),
contains = "Versioned",
prototype = prototype(
rtime = numeric(),
intensity = numeric(),
mz = c(NA_real_, NA_real_),
filterMz = c(NA_real_, NA_real_),
precursorMz = c(NA_real_, NA_real_),
productMz = c(NA_real_, NA_real_),
fromFile = integer(),
aggregationFun = character(),
msLevel = 1L
),
validity = function(object)
.validChromatogram(object)
)
#' @title Container for multiple Chromatogram objects
#'
#' @aliases coerce,matrix,MChromatograms-method
#'
#' @description The \code{MChromatograms} class allows to store
#' \code{\link{Chromatogram}} objects in a \code{matrix}-like
#' two-dimensional structure.
#'
#' @details The \code{MChromatograms} class extends the base \code{matrix} class
#' and hence allows to store \code{\link{Chromatogram}} objects in a
#' two-dimensional array. Each row is supposed to contain
#' \code{Chromatogram} objects for one MS data \emph{slice} with a common
#' m/z and rt range. Columns contain \code{Chromatogram} objects from the
#' same sample.
#'
#' @export
#'
#' @rdname MChromatograms-class
#'
#' @seealso \code{\link{Chromatogram}} for the class representing chromatogram
#' data.
#' \code{\link{chromatogram}} for the method to extract a
#' \code{MChromatograms} object from a \code{\linkS4class{MSnExp}} or
#' \code{\linkS4class{OnDiskMSnExp}} object.
#' \code{\link{readSRMData}} for the function to read chromatographic data
#' of an SRM/MRM experiment.
#'
#' @author Johannes Rainer
#'
#' @examples
#' ## Creating some chromatogram objects to put them into a MChromatograms object
#' ints <- abs(rnorm(25, sd = 200))
#' ch1 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(32, sd = 90))
#' ch2 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(19, sd = 120))
#' ch3 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(21, sd = 40))
#' ch4 <- Chromatogram(rtime = 1:length(ints), ints)
#'
#' ## Create a MChromatograms object with 2 rows and 2 columns
#' chrs <- MChromatograms(list(ch1, ch2, ch3, ch4), nrow = 2)
#' chrs
#'
#' ## Extract the first element from the second column. Extracting a single
#' ## element always returns a Chromatogram object.
#' chrs[1, 2]
#'
#' ## Extract the second row. Extracting a row or column (i.e. multiple elements
#' ## returns by default a list of Chromatogram objects.
#' chrs[2, ]
#'
#' ## Extract the second row with drop = FALSE, i.e. return a MChromatograms
#' ## object.
#' chrs[2, , drop = FALSE]
#'
#' ## Replace the first element.
#' chrs[1, 1] <- ch3
#' chrs
#'
#' ## Add a pheno data.
#' pd <- data.frame(name = c("first sample", "second sample"),
#' idx = 1:2)
#' pData(chrs) <- pd
#'
#' ## Column names correspond to the row names of the pheno data
#' chrs
#'
#' ## Access a column within the pheno data
#' chrs$name
#'
#' ## Access the m/z ratio for each row; this will be NA for the present
#' ## object
#' mz(chrs)
setClass("MChromatograms",
contains = "matrix",
slots = c(phenoData = "AnnotatedDataFrame",
featureData = "AnnotatedDataFrame"),
prototype = prototype(
matrix(ncol = 0, nrow = 0),
phenoData = new("AnnotatedDataFrame",
dimLabels = c("sampleNames", "sampleColumns")),
featureData = new("AnnotatedDataFrame",
dimLabels = c("featureNames", "featureColumns"))
),
validity = function(object)
.validMChromatograms(object)
)
#' @name MSpectra
#'
#' @aliases MSpectra-class show,MSpectra-method coerce,MSpectra,list-method coerce,MSpectra,MSnExp-method
#'
#' @title List of Spectrum objects along with annotations
#'
#' @description
#'
#' `MSpectra` (Mass Spectra) objects allow to collect one or more
#' [Spectrum-class] object(s) ([Spectrum1-class] or [Spectrum2-class]) in
#' a `list`-like structure with the possibility to add arbitrary annotations
#' to each individual `Spectrum` object. These can be accessed/set with
#' the [mcols()] method.
#'
#' `MSpectra` objects can be created with the `MSpectra` function.
#'
#' Functions to access the individual spectra's attributes are available
#' (listed below).
#'
#' @details
#'
#' `MSpectra` inherits all methods from the [SimpleList] class of the
#' `S4Vectors` package. This includes `lapply` and other data manipulation
#' and subsetting operations.
#'
#' @param object For all functions: a `MSpectra` object.
#'
#' @param x For all functions: a `MSpectra` object.
#'
#' @md
#'
#' @rdname MSpectra
NULL
.MSpectra <- setClass("MSpectra",
contains = "SimpleList",
prototype = prototype(elementType = "Spectrum")
)
setValidity("MSpectra", function(object) {
## All elements in the list have to be Spectrum objects.
msg <- character()
if (any(vapply(object, function(z) !is(z, "Spectrum"), logical(1))))
msg <- c(msg, "All elements have to be Spectrum objects")
if (length(msg)) msg else TRUE
})
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Defines functions getClassVersionString getClassVersion
## Returns of class version as documented in .MSnBaseEnd$ClassVersions
## as and instance of class Versions.
getClassVersion <- function(x) {
if (!is.character(x))
x <- class(x)[1]
## This get class versions from parent classes (if any)
ver <- classVersion(x)
## Adds (or overwrites) x's class version to the list of class
## versions
ver[x] <- getClassVersionString(x)
ver
}
## Utility to just extract the version string from the environment.
getClassVersionString <- function(x) {
if (!is.character(x))
x <- class(x)[1]
return(.MSnbaseEnv$ClassVersions[x])
}
######################################################################
## MSnProcess: Container for MSnExp and MSnSet processing information
## See online documentation for more information.
setClass("MSnProcess",
representation = representation(
files = "character",
processing = "character",
merged = "logical",
cleaned = "logical",
removedPeaks = "character",
smoothed = "logical",
trimmed = "numeric",
normalised = "logical",
MSnbaseVersion = "character"),
contains = c("Versioned"),
prototype = prototype(
new("Versioned", versions = c(MSnProcess = "0.1.3")),
processing = character(),
files = character(),
trimmed = numeric(),
removedPeaks = character(),
MSnbaseVersion = character())) ## set in initialize()
#################################################################
## The 'Minimum Information About a Proteomics Experiment' Class
## See online documentation for more information.
setClass("MIAPE",
representation = representation(
title = "character",
url = "character",
## Publication details
abstract = "character",
pubMedIds = "character",
## Other useful slots, from MIAME
samples = "list",
preprocessing = "list",
other = "list",
## ########################
## Based on MIAPE-MS 2.24
## will be updated with MIAPE-MSI and MIAPE-Quant
## 1. General features - (a) Global descriptors
dateStamp = "character",
## Responsible person
name = "character",
lab = "character",
contact = "character",
email = "character",
## Instrument details
instrumentModel = "character",
instrumentManufacturer = "character",
instrumentCustomisations = "character",
## 1. General features - (b) Control and analysis software
softwareName = "character",
softwareVersion = "character",
switchingCriteria = "character",
isolationWidth = "numeric",
parameterFile = "character",
## 2. Ion sources -- will be updated to
## provided details specific to
## different sources
ionSource = "character", ## ESI, MALDI, ...
ionSourceDetails = "character",
## 3. Post-source component
analyser = "character", ## Quad, TOF, Trap, ...
analyserDetails = "character",
## 3. Post-source component - (d) Collision cell
collisionGas = "character",
collisionPressure = "numeric",
collisionEnergy = "character",
## 3. Post-source component - (f) Detectors
detectorType = "character",
detectorSensitivity = "character"
## 4. Spectrum and peak list generation and annotation
## (a) Spectrum description
## (b) Peak list generation
## (c) Quantitation for selected ions
## -- see Spectrum and MSnProcess objects
),
contains = c("MIAxE"),
prototype = prototype(
new("Versioned",
versions = c(classVersion("MIAxE"), MIAPE = "0.2.2")),
name = "",
lab = "",
contact = "",
title = "",
abstract = "",
url = "",
pubMedIds = "",
email = "",
samples = list(),
preprocessing = list(),
other = list()))
#############################################################################
## pSet: similarly to eSet but with a focus toward proteomics experiments,
## pSet is a VIRTUAL class containing assay data (typically, one or many
## different sets of spectra), phenotypic data (describing the samples involved
## in the experiment), experimental data (describing the methods and
## protocols used) and feature data (describing the features in the assay).
##
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
setClass("pSet",
representation(assayData = "environment", ## locked environment
phenoData = "AnnotatedDataFrame",
featureData = "AnnotatedDataFrame",
# How to link individual spectra in assayData env to featureData?
# The spectra and the rowNames of featureData will be named X1..(n1+n2+...)
experimentData = "MIAxE",
protocolData = "AnnotatedDataFrame",
processingData = "MSnProcess",
##onDisk = "logical",
.cache = "environment", ## locked
"VIRTUAL"),
contains = "Versioned",
prototype = prototype(
new("Versioned", versions = c(pSet = "0.1.1")),
assayData = new.env(parent=emptyenv()),
experimentData = new("MIAPE"),
phenoData = new("AnnotatedDataFrame",
dimLabels=c("sampleNames", "fileNumbers")),
featureData = new("AnnotatedDataFrame",
dimLabels=c("featureNames", "featureColumns")),
protocolData = new("AnnotatedDataFrame",
dimLabels=c("sampleNames", "sampleColumns"))
##,onDisk = FALSE
)
)
##################################################################
## Container for MSn Experiments Data and Meta-Data
## See online documentation for more information.
setClass("MSnExp",
contains=c("pSet"),
prototype = prototype(
new("VersionedBiobase",
versions = c(classVersion("pSet"), MSnExp="0.3.1")),
experimentData = new("MIAPE"))
)
###################################################################
## The Spectrum class and it's sub-classes Spectrum1 and Spectrum2
## See online documentation for more information.
setClass("Spectrum",
representation = representation(
msLevel="integer",
peaksCount="integer",
rt="numeric",
acquisitionNum="integer",
scanIndex = "integer",
tic = "numeric",
mz = "numeric",
intensity = "numeric",
fromFile = "integer",
centroided = "logical",
smoothed = "logical",
polarity="integer",
"VIRTUAL"),
contains=c("Versioned"),
prototype = prototype(
rt = numeric(),
polarity = NA_integer_,
acquisitionNum = NA_integer_,
msLevel = NA_integer_,
centroided = NA,
smoothed = NA,
peaksCount = 0L,
tic = 0,
scanIndex = integer(),
mz = numeric(),
intensity = numeric()),
validity = function(object)
validSpectrum(object)
)
setClass("Spectrum2",
representation = representation(
merged="numeric",
precScanNum="integer",
precursorMz="numeric",
precursorIntensity = "numeric",
precursorCharge = "integer",
collisionEnergy = "numeric"),
contains=c("Spectrum"),
prototype = prototype(
merged = 1,
acquisitionNum = integer(),
precScanNum = integer(),
precursorMz = numeric(),
precursorIntensity = numeric(),
msLevel = as.integer(2),
precursorCharge = integer(),
collisionEnergy = numeric()),
validity = function(object) {
msg <- validMsg(NULL, NULL)
msl <- object@msLevel
if (msl < as.integer(2))
msg <- validMsg(msg,
paste0("Object of class ",
class(object),
" but msLevel is ", msl,
" (should be > 1)"))
if (is.null(msg)) TRUE
else msg
})
setClass("Spectrum1",
contains=c("Spectrum"),
prototype = prototype(
polarity=integer(),
msLevel = as.integer(1)),
validity = function(object) {
msg <- validMsg(NULL, NULL)
msl <- object@msLevel
if (msl!=as.integer(1))
msg <- validMsg(msg,paste("Object of class",class(object),
"but msLevel is",msl,sep=" "))
if (is.null(msg)) TRUE
else msg
})
##################################################################
## Data Structure for Reporter Ions for labelled MS Quantification
## See online documentation for more information.
setClass("ReporterIons",
representation = representation(
name = "character",
reporterNames = "character",
description = "character",
mz = "numeric",
col = "character",
width = "numeric"),
contains = c("Versioned"),
prototype = prototype(
new("Versioned", versions = c(ReporterIons = "0.1.0")),
name = character(),
reporterNames = character(),
description = character(),
mz = numeric(),
col = character(),
width = numeric()),
validity = function(object) {
msg <- validMsg(NULL, NULL)
if (length(object@mz) == 0) {
msg <- validMsg(msg, "No reporter ions defined.")
} else {
if (length(object@col) != length(object@mz))
warning("Missing color(s) for the reporter ions.")
}
if (is.null(msg)) TRUE
else msg
})
#####################################################################
## The "MSnSet" Class for MS Proteomics Expression Data and Meta-Data
## See online documentation for more information.
setClass("MSnSet",
representation = representation(
experimentData="MIAPE",
processingData = "MSnProcess",
qual = "data.frame"),
contains = "eSet",
prototype = prototype(
new("VersionedBiobase",
versions=c(classVersion("eSet"),
classVersion("pSet"),
MSnSet="0.4.0")),
experimentData=new("MIAPE"),
annotation="No feature annotation."))
.MSnSetList <-
setClass("MSnSetList",
slots = c(x = "list",
log = "list",
featureData = "DataFrame"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(MSnSetList = "0.2.0"))),
validity = function(object) {
msg <- validMsg(NULL, NULL)
if (!listOf(object@x, "MSnSet", valid = FALSE))
msg <- validMsg(msg, "Not all items are MSnSets.")
nvals <- sapply(object@x, validObject)
if (!all(nvals))
msg <- validMsg(msg,
paste(sum(!nvals),
"MSnSets are not valid."))
if (length(object@x) != nrow(object@featureData))
msg <- validMsg(msg,
"Data and meta-data dimensions don't match.")
if (length(object@x) &&
!identical(names(object@x), rownames(object@featureData)))
msg <- validMsg(msg,
"Data and meta-data names don't match.")
if (is.null(msg)) TRUE
else msg
})
#####################################################################
## Features of interest infrastructure
.FeaturesOfInterest <-
setClass("FeaturesOfInterest",
slots = c(
description = "character",
fnames = "character",
date = "character",
objpar = "list"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(FeaturesOfInterest = "0.1.0"))))
.FoICollection <-
setClass("FoICollection",
slots = c(foic = "list"),
contains = "Versioned",
prototype = prototype(
new("Versioned",
versions = c(FeaturesOfInterest = "0.1.0"))))
#####################################################################
## Support for the PSI mzTab format
.MzTab <- setClass("MzTab",
slots = c(
Metadata = "list",
Filename = "character",
Proteins = "data.frame",
Peptides = "data.frame",
PSMs = "data.frame",
SmallMolecules = "data.frame",
MoleculeFeatures = "data.frame",
MoleculeEvidence = "data.frame",
Comments = "character"))
##################################################################
## Container for MSn Experiments Data and Meta-Data enabling/allowing
## to process raw MS files on-the-fly (without the need to keep all
## data in memory).
setClass("OnDiskMSnExp",
representation=representation(
spectraProcessingQueue="list", ## List collecting ProcessingSteps for lazy processing.
backend="character" ## That's to eventually add a SQLite backend later...
),
contains=c("MSnExp"),
prototype = prototype(
new("VersionedBiobase",
versions = c(classVersion("MSnExp"), OnDiskMSnExp="0.0.1")),
spectraProcessingQueue=list(),
backend=character()),
validity=function(object){
## Return true if the object is empty.
if (length(object) == 0)
return(TRUE)
## Ensure that the files (returned by fileNames) are available
## and check also that the featureData contains all the required
## information.
msg <- validMsg(NULL, NULL)
## Elements in spectraProcessingQueue have to be ProcessingStep objects.
if (length(object@spectraProcessingQueue) > 0){
isOK <- unlist(lapply(object@spectraProcessingQueue,
function(z)
return(is(z, "ProcessingStep"))))
if (any(!isOK))
msg <- validMsg(msg,
paste0("Only objects of type 'ProcessingStep'",
" allowed in slot 'spectraProcessingQueue'"))
}
## Check that required columns are present in the featureData:
msg <- validMsg(msg, validateFeatureDataForOnDiskMSnExp(featureData(object)))
## Check if the files do exist.
theFiles <- fileNames(object)
for (theF in theFiles){
if (!file.exists(theF))
msg <- validMsg(msg,
paste0("Required data file '", basename(theF),
"' not found!"))
}
## Some last checks I had to take from the pSet as validObject on OnDiskMSnExp was first
## calling the pSet validate method on the MSnExp and that caused a problem since we don't
## have an assayData with spectra here (fromFile was trying to get that form there).
aFileIds <- fromFile(object)
fFileIds <- fData(object)$fileIdx
if (length(fFileIds) && any(aFileIds != fFileIds))
msg <- validMsg(msg, "Mismatch of files in assayData and processingData.")
## Check if the fromFile values match to @files in processingData
filesProcData <- 1:length(processingData(object)@files)
if ( !all(unique(sort(aFileIds)) == unique(sort(filesProcData))) )
msg <- validMsg(msg, "Spectra file indices in assayData does not match files in processinData.")
nfilesprocData <- length(processingData(object)@files)
nfilesSpectra <- length(unique(aFileIds))
if (nfilesprocData < nfilesSpectra)
msg <- validMsg(msg, "More spectra files in assayData than in processingData.")
if (length(sampleNames(object)) != nrow(pData(object)))
msg <- validMsg(msg, "Different number of samples accoring to sampleNames and pData.")
## Check also experimentData:
if (length(
unique(c(length(fileNames(object)),
length(experimentData(object)@instrumentManufacturer),
length(experimentData(object)@instrumentModel),
length(experimentData(object)@ionSource),
length(experimentData(object)@analyser),
length(experimentData(object)@detectorType)))) != 1)
msg <- validMsg(msg, "The number of files does not match the information in experimentData.")
if (!isOnDisk(object))
msg <- validMsg(msg, "Object is not 'onDisk'.")
if (!isEmpty(object@assayData))
msg <- validMsg(msg, "Assaydata is not empty.")
if (is.null(msg)) {
return(TRUE)
} else {
return(msg)
}
})
############################################################
## Simple class defining a "processing" step, consisting of a
## function name (@FUN) and optional arguments (@ARG)
setClass("ProcessingStep",
representation=representation(
FUN="character",
ARGS="list"
),
contains="Versioned",
prototype=prototype(
new("Versioned",
versions=c(ProcessingStep="0.0.1")),
ARGS=list(),
FUN=character()
),
validity=function(object){
msg <- validMsg(NULL, NULL)
## Check if function/method exists?
if(length(object@FUN) > 0){
Res <- try(get(object@FUN), silent=TRUE)
if(is(Res, "try-error")){
msg <- validMsg(msg,
paste0("Function '", object@FUN, "' not found!"))
}else{
if(!is(Res, "function"))
msg <- validMsg(msg,
paste0("'", object@FUN, "' is not a function!"))
}
}
if(is.null(msg)){
return(TRUE)
}else{
return(msg)
}
})
#' @title Representation of chromatographic MS data
#'
#' @description The \code{Chromatogram} class is designed to store
#' chromatographic MS data, i.e. pairs of retention time and intensity
#' values. Instances of the class can be created with the
#' \code{Chromatogram} constructor function but in most cases the dedicated
#' methods for \code{\linkS4class{OnDiskMSnExp}} and
#' \code{\linkS4class{MSnExp}} objects extracting chromatograms should be
#' used instead (i.e. the \code{\link{chromatogram}} method).
#'
#' @details The \code{mz}, \code{filterMz}, \code{precursorMz} and
#' \code{productMz} are stored as a \code{numeric(2)} representing a range
#' even if the chromatogram was generated for only a single ion (i.e. a
#' single mz value). Using ranges for \code{mz} values allow this class to
#' be used also for e.g. total ion chromatograms or base peak chromatograms.
#'
#' The slots \code{precursorMz} and \code{productMz} allow to represent SRM
#' (single reaction monitoring) and MRM (multiple SRM) chromatograms. As
#' example, a \code{Chromatogram} for a SRM transition 273 -> 153 will have
#' a \code{@precursorMz = c(273, 273)} and a
#' \code{@productMz = c(153, 153)}.
#'
#' @rdname Chromatogram-class
#'
#' @export
#'
#' @seealso \code{\link{MChromatograms}} for combining \code{Chromatogram} in
#' a two-dimensional matrix (rows being mz-rt ranges, columns samples).
#' \code{\link{chromatogram}} for the method to extract chromatogram data
#' from a \code{\linkS4class{MSnExp}} or \code{\linkS4class{OnDiskMSnExp}}
#' object.
#' \code{\link{clean}} for the method to \emph{clean} a \code{Chromatogram}
#' object.
#'
#' @author Johannes Rainer
#'
#' @examples
#'
#' ## Create a simple Chromatogram object.
#' ints <- abs(rnorm(100, sd = 100))
#' rts <- seq_len(length(ints))
#' chr <- Chromatogram(rtime = rts, intensity = ints)
#' chr
#'
#' ## Extract intensities
#' intensity(chr)
#'
#' ## Extract retention times
#' rtime(chr)
#'
#' ## Extract the mz range - is NA for the present example
#' mz(chr)
#'
#' ## plot the Chromatogram
#' plot(chr)
setClass("Chromatogram",
slots = c(
rtime = "numeric",
intensity = "numeric",
mz = "numeric",
filterMz = "numeric",
precursorMz = "numeric", ## Or call that Q1mz?
productMz = "numeric", ## Or call that Q3mz?
fromFile = "integer",
aggregationFun = "character",
msLevel = "integer"
),
contains = "Versioned",
prototype = prototype(
rtime = numeric(),
intensity = numeric(),
mz = c(NA_real_, NA_real_),
filterMz = c(NA_real_, NA_real_),
precursorMz = c(NA_real_, NA_real_),
productMz = c(NA_real_, NA_real_),
fromFile = integer(),
aggregationFun = character(),
msLevel = 1L
),
validity = function(object)
.validChromatogram(object)
)
#' @title Container for multiple Chromatogram objects
#'
#' @aliases coerce,matrix,MChromatograms-method
#'
#' @description The \code{MChromatograms} class allows to store
#' \code{\link{Chromatogram}} objects in a \code{matrix}-like
#' two-dimensional structure.
#'
#' @details The \code{MChromatograms} class extends the base \code{matrix} class
#' and hence allows to store \code{\link{Chromatogram}} objects in a
#' two-dimensional array. Each row is supposed to contain
#' \code{Chromatogram} objects for one MS data \emph{slice} with a common
#' m/z and rt range. Columns contain \code{Chromatogram} objects from the
#' same sample.
#'
#' @export
#'
#' @rdname MChromatograms-class
#'
#' @seealso \code{\link{Chromatogram}} for the class representing chromatogram
#' data.
#' \code{\link{chromatogram}} for the method to extract a
#' \code{MChromatograms} object from a \code{\linkS4class{MSnExp}} or
#' \code{\linkS4class{OnDiskMSnExp}} object.
#' \code{\link{readSRMData}} for the function to read chromatographic data
#' of an SRM/MRM experiment.
#'
#' @author Johannes Rainer
#'
#' @examples
#' ## Creating some chromatogram objects to put them into a MChromatograms object
#' ints <- abs(rnorm(25, sd = 200))
#' ch1 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(32, sd = 90))
#' ch2 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(19, sd = 120))
#' ch3 <- Chromatogram(rtime = 1:length(ints), ints)
#' ints <- abs(rnorm(21, sd = 40))
#' ch4 <- Chromatogram(rtime = 1:length(ints), ints)
#'
#' ## Create a MChromatograms object with 2 rows and 2 columns
#' chrs <- MChromatograms(list(ch1, ch2, ch3, ch4), nrow = 2)
#' chrs
#'
#' ## Extract the first element from the second column. Extracting a single
#' ## element always returns a Chromatogram object.
#' chrs[1, 2]
#'
#' ## Extract the second row. Extracting a row or column (i.e. multiple elements
#' ## returns by default a list of Chromatogram objects.
#' chrs[2, ]
#'
#' ## Extract the second row with drop = FALSE, i.e. return a MChromatograms
#' ## object.
#' chrs[2, , drop = FALSE]
#'
#' ## Replace the first element.
#' chrs[1, 1] <- ch3
#' chrs
#'
#' ## Add a pheno data.
#' pd <- data.frame(name = c("first sample", "second sample"),
#' idx = 1:2)
#' pData(chrs) <- pd
#'
#' ## Column names correspond to the row names of the pheno data
#' chrs
#'
#' ## Access a column within the pheno data
#' chrs$name
#'
#' ## Access the m/z ratio for each row; this will be NA for the present
#' ## object
#' mz(chrs)
setClass("MChromatograms",
contains = "matrix",
slots = c(phenoData = "AnnotatedDataFrame",
featureData = "AnnotatedDataFrame"),
prototype = prototype(
matrix(ncol = 0, nrow = 0),
phenoData = new("AnnotatedDataFrame",
dimLabels = c("sampleNames", "sampleColumns")),
featureData = new("AnnotatedDataFrame",
dimLabels = c("featureNames", "featureColumns"))
),
validity = function(object)
.validMChromatograms(object)
)
#' @name MSpectra
#'
#' @aliases MSpectra-class show,MSpectra-method coerce,MSpectra,list-method coerce,MSpectra,MSnExp-method
#'
#' @title List of Spectrum objects along with annotations
#'
#' @description
#'
#' `MSpectra` (Mass Spectra) objects allow to collect one or more
#' [Spectrum-class] object(s) ([Spectrum1-class] or [Spectrum2-class]) in
#' a `list`-like structure with the possibility to add arbitrary annotations
#' to each individual `Spectrum` object. These can be accessed/set with
#' the [mcols()] method.
#'
#' `MSpectra` objects can be created with the `MSpectra` function.
#'
#' Functions to access the individual spectra's attributes are available
#' (listed below).
#'
#' @details
#'
#' `MSpectra` inherits all methods from the [SimpleList] class of the
#' `S4Vectors` package. This includes `lapply` and other data manipulation
#' and subsetting operations.
#'
#' @param object For all functions: a `MSpectra` object.
#'
#' @param x For all functions: a `MSpectra` object.
#'
#' @md
#'
#' @rdname MSpectra
NULL
.MSpectra <- setClass("MSpectra",
contains = "SimpleList",
prototype = prototype(elementType = "Spectrum")
)
setValidity("MSpectra", function(object) {
## All elements in the list have to be Spectrum objects.
msg <- character()
if (any(vapply(object, function(z) !is(z, "Spectrum"), logical(1))))
msg <- c(msg, "All elements have to be Spectrum objects")
if (length(msg)) msg else TRUE
})
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