,
Goncalo Correia [aut] ,
Jake Pearce [ctb],
Caroline Sands [ctb]| Title: | Peak Picking and Annotation of High Resolution Experiments |
|---|---|
| Description: | An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. |
| Authors: | Arnaud Wolfer [aut, cre] ,
Goncalo Correia [aut] ,
Jake Pearce [ctb],
Caroline Sands [ctb] |
| Maintainer: | Arnaud Wolfer <[email protected]> |
| License: | GPL-3 |
| Version: | 1.19.0 |
| Built: | 2024-09-15 06:07:10 UTC |
| Source: | https://github.com/bioc/peakPantheR |
extract parts of peakPantheRAnnotation class
## S4 method for signature 'peakPantheRAnnotation,ANY,ANY,ANY' x[i, j, drop = "missing"]## S4 method for signature 'peakPantheRAnnotation,ANY,ANY,ANY' x[i, j, drop = "missing"]
x |
object from which to extract element(s) or in which to replace element(s). |
i |
(sample) indices specifying elements to extract or replace |
j |
(compound) indices specifying elements to extract or replace |
drop |
not applicable |
(peakPantheRAnnotation) object subset
acquisitionTime accessor returns value as.POSIXct
## S4 method for signature 'peakPantheRAnnotation' acquisitionTime(object)## S4 method for signature 'peakPantheRAnnotation' acquisitionTime(object)
object |
peakPantheRAnnotation |
(POSIXct) A character vector of acquisition date-time (converted from POSIXct) or NA
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## acquisitionTime can only be extracted from NetCDF files acquisitionTime(annotation) # [1] NA NA NA }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## acquisitionTime can only be extracted from NetCDF files acquisitionTime(annotation) # [1] NA NA NA }
Return a ggplot object of a feature diagnostic multiplot
annotation_diagnostic_multiplot_UI_helper( cpdNb, annotation, splNum = NULL, splColrColumn = NULL, ... )annotation_diagnostic_multiplot_UI_helper( cpdNb, annotation, splNum = NULL, splColrColumn = NULL, ... )
cpdNb |
(int) position of the feature to extract (1 to nbCpd) |
annotation |
(peakPantheRAnnotation) Annotation object |
splNum |
(int) NULL or number of spectra to plot, chosen randomly from all spectra. If NULL or equal to the total number of spectra, plot all spectra |
splColrColumn |
(str) NULL, None or a spectraMetadata column for colouring each sample |
... |
Additional parameters for plotting |
(ggplotObject) Diagnostic multiplot for a feature
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Plot of an empty annotation annotation_diagnostic_multiplot_UI_helper(cpdNb = 2, annotation = emptyAnnotation, splNum = NULL, splColrColumn = NULL) # Warning: the object has not been annotated, return an empty diagnostic # plot list## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Plot of an empty annotation annotation_diagnostic_multiplot_UI_helper(cpdNb = 2, annotation = emptyAnnotation, splNum = NULL, splColrColumn = NULL) # Warning: the object has not been annotated, return an empty diagnostic # plot list
Return a table of fit statistic (ratio of peaks found, ratio of peaks filled, ppm error, RT deviation)
annotation_fit_summary_UI_helper(annot)annotation_fit_summary_UI_helper(annot)
annot |
(peakPantheRAnnotation) Annotation object |
(data.frame) Fit statistics
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # statistics of an empty annotation annotation_fit_summary_UI_helper(emptyAnnotation) # Ratio peaks found (%) Ratio peaks filled (%) ppm error # ID-1 - Cpd 1 NA 0 NaN # ID-2 - Cpd 2 NA 0 NaN # RT deviation (s) # ID-1 - Cpd 1 NaN # ID-2 - Cpd 2 NaN## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # statistics of an empty annotation annotation_fit_summary_UI_helper(emptyAnnotation) # Ratio peaks found (%) Ratio peaks filled (%) ppm error # ID-1 - Cpd 1 NA 0 NaN # ID-2 - Cpd 2 NA 0 NaN # RT deviation (s) # ID-1 - Cpd 1 NaN # ID-2 - Cpd 2 NaN
show method specific to the UI, that returns each field in a named list to ease display
annotation_showMethod_UI_helper(annotation)annotation_showMethod_UI_helper(annotation)
annotation |
(peakPantherAnnotation) Object to describe |
(list) Named list of annotation properties
# Initialise an empty annotation, no uROI, no use of FIR annotInit <- peakPantheRAnnotation() # return properties annotation_showMethod_UI_helper(annotInit) # $nbCompounds # [1] 0 # # #$nbSamples # [1] 0 # # #$uROIExist # [1] FALSE # # $useUROI # [1] FALSE # # $useFIR # [1] FALSE # # $isAnnotated # [1] FALSE# Initialise an empty annotation, no uROI, no use of FIR annotInit <- peakPantheRAnnotation() # return properties annotation_showMethod_UI_helper(annotInit) # $nbCompounds # [1] 0 # # #$nbSamples # [1] 0 # # #$uROIExist # [1] FALSE # # $useUROI # [1] FALSE # # $useFIR # [1] FALSE # # $isAnnotated # [1] FALSE
Return a text description of an annotation for UI presentation
annotation_showText_UI_helper(annotProp)annotation_showText_UI_helper(annotProp)
annotProp |
(list) Named list of annotation properties as
created by |
(str) Textual description of the annotation to show on UI
# Input properties_default <- list(nbCompounds = 0, nbSamples = 0, uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, isAnnotated = FALSE) # Generate description annotation_showText_UI_helper(properties_default) # [[1]] # [1] "Not annotated" # # [[2]] # [1] "0 compounds" # # [[3]] # [1] "0 samples" # # [[4]] # [1] "updated ROI do not exist (uROI)" # # [[5]] # [1] "does not use updated ROI (uROI)" # # [[6]] # [1] "does not use fallback integration regions (FIR)"# Input properties_default <- list(nbCompounds = 0, nbSamples = 0, uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, isAnnotated = FALSE) # Generate description annotation_showText_UI_helper(properties_default) # [[1]] # [1] "Not annotated" # # [[2]] # [1] "0 compounds" # # [[3]] # [1] "0 samples" # # [[4]] # [1] "updated ROI do not exist (uROI)" # # [[5]] # [1] "does not use updated ROI (uROI)" # # [[6]] # [1] "does not use fallback integration regions (FIR)"
Generate a multiplot of all diagnostic plots (as generated by
annotationDiagnosticPlots()) for each compound
annotationDiagnosticMultiplot(annotationDiagnosticPlotList)annotationDiagnosticMultiplot(annotationDiagnosticPlotList)
annotationDiagnosticPlotList |
(list) List of (one per compound) of list
of diagnostic plots as generated by |
A list of multiplots (one per compound)
Generate fit diagnostic plots for each ROI: EICFit the
raw data and detected feature fit, rtPeakwidthVert detected peaks
retention time apex and peakwidth (vertical and no run order),
rtPeakwidthHorzRunOrder detected peaks retention time apex and
peakwidth by run order, mzPeakwidthHorzRunOrder detected peaks m/z
apex and peakwidth by run order, areaRunOrder detected peaks area by
run order, rtHistogram histogram of detected peaks retention time,
mzHistogram histogram of detected peaks m/z, areaHistogram
histogram of detected peaks area.
## S4 method for signature 'peakPantheRAnnotation' annotationDiagnosticPlots( object, sampleColour, sampling, verbose )## S4 method for signature 'peakPantheRAnnotation' annotationDiagnosticPlots( object, sampleColour, sampling, verbose )
object |
(peakPantheRAnnotation) Annotated peakPantheRAnnotation object |
sampleColour |
(str) NULL or vector colour for each sample |
sampling |
(int) Number of points to employ when plotting fittedCurve |
verbose |
(bool) if TRUE message the plot generation progress |
A list (one list per compound) of diagnostic plots:
result[[i]]$EICFit, result[[i]]$rtPeakwidthVert,
result[[i]]$rtPeakwidthHorzRunOrder,
result[[i]]$mzPeakwidthHorzRunOrder, result[[i]]$areaRunOrder,
result[[i]]$rtHistogram, result[[i]]$mzHistogram,
result[[i]]$areaHistogram, result[[i]]$title
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotationDiagnosticPlots(emptyAnnotation) # Warning: the object has not been annotated, return an empty diagnostic plot # list # [[1]] # NULL # [[2]] # NULL }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotationDiagnosticPlots(emptyAnnotation) # Warning: the object has not been annotated, return an empty diagnostic plot # list # [[1]] # NULL # [[2]] # NULL }
Set updated ROI (uROI) and Fallback Integration Regions (FIR) based on the annotation results. If the object is not annotated, it is returned untouched. ROI is not modified. If uROI exist they are left untouched, otherwise they are set as the minimum and maximum found peaks limits (+/-5% of ROI in retention time). If FIR are used they are left untouched, otherwise they are set as the median of the found limits (rtMin, rtMax, mzMin, mzMax).
## S4 method for signature 'peakPantheRAnnotation' annotationParamsDiagnostic( object, verbose )## S4 method for signature 'peakPantheRAnnotation' annotationParamsDiagnostic( object, verbose )
object |
(peakPantheRAnnotation) Annotated peakPantheRAnnotation object |
verbose |
(bool) If TRUE message progress of uROI and FIR calculation |
(peakPantheRAnnotation) object with updated ROI and FIR set from annotation results
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotationParamsDiagnostic(emptyAnnotation, verbose=TRUE) # Warning: the object has not been annotated, return the object untouched # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotationParamsDiagnostic(emptyAnnotation, verbose=TRUE) # Warning: the object has not been annotated, return the object untouched # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated }
annotationTable returns a dataframe (row samples, col compounds) filled with a specific peakTable column
## S4 method for signature 'peakPantheRAnnotation' annotationTable(object, column)## S4 method for signature 'peakPantheRAnnotation' annotationTable(object, column)
object |
peakPantheRAnnotation |
column |
a peakTable columns |
(data.frame) (row samples, col compounds) filled with a specific peakTable column
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation annotationTable(annotation) # ID-1 ID-2 # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF NA NA # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF NA NA # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF NA NA }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation annotationTable(annotation) # ID-1 ID-2 # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF NA NA # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF NA NA # C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF NA NA }
cpdID accessor
## S4 method for signature 'peakPantheRAnnotation' cpdID(object)## S4 method for signature 'peakPantheRAnnotation' cpdID(object)
object |
peakPantheRAnnotation |
(str) A character vector of compound IDs, of length number of compounds
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) cpdID(annotation) # [1] 'ID-1' 'ID-2' }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) cpdID(annotation) # [1] 'ID-1' 'ID-2' }
cpdMetadata accessor
## S4 method for signature 'peakPantheRAnnotation' cpdMetadata(object)## S4 method for signature 'peakPantheRAnnotation' cpdMetadata(object)
object |
peakPantheRAnnotation |
(data.frame) A data.frame of compound metadata, with compounds as row and metadata as columns
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values not initialised cpdMetadata(annotation) # data frame with 0 columns and 2 rows }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values not initialised cpdMetadata(annotation) # data frame with 0 columns and 2 rows }
cpdName accessor
## S4 method for signature 'peakPantheRAnnotation' cpdName(object)## S4 method for signature 'peakPantheRAnnotation' cpdName(object)
object |
peakPantheRAnnotation |
(str) A character vector of compound names, of length number of compounds
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) cpdName(annotation) # [1] 'Cpd 1' 'Cpd 2' }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) cpdName(annotation) # [1] 'Cpd 1' 'Cpd 2' }
dataPoints accessor
## S4 method for signature 'peakPantheRAnnotation' dataPoints(object)## S4 method for signature 'peakPantheRAnnotation' dataPoints(object)
object |
peakPantheRAnnotation |
A list of length number of spectra files. Each list element is a
ROIsDataPoint list of data.frame of raw data points for each
ROI/uROI (retention time 'rt', mass 'mz' and intensity 'int' (as column) of
each raw data points (as row))
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation dataPoints(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation dataPoints(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }
EICs accessor
## S4 method for signature 'peakPantheRAnnotation' EICs(object, aggregationFunction)## S4 method for signature 'peakPantheRAnnotation' EICs(object, aggregationFunction)
object |
peakPantheRAnnotation |
aggregationFunction |
(str) Function to use in order to aggregate
intensities across mz in each scan. One of |
(float) Extracted Ion Chromatogram aggregated across mz in each scan
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation EICs(annotation) # [[1]] # list() # [[2]] # list() # [[3]] # list() }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation EICs(annotation) # [[1]] # list() # [[2]] # list() # [[3]] # list() }
Guess function for initial exponentially modified gaussian parameters and bounds, at the moment only checks the x position
emgGaussian_guess(x, y)emgGaussian_guess(x, y)
x |
(numeric) x values (e.g. retention time) |
y |
(numeric) y observed values (e.g. spectra intensity) |
A list of guessed starting parameters list()$init_params,
lower list()$lower_bounds and upper bounds list()$upper_bounds
($gamma, $center, $sigma, $amplitude)
Implementation of the Exponentially Modified Gaussian (EMG) peak shape for use with minpack.lm
emgGaussian_minpack.lm(params, xx)emgGaussian_minpack.lm(params, xx)
params |
(list) exponentiall modified gaussian parameters
( |
xx |
(numeric) values at which to evalute the exponentially modified gaussian |
value of the exponentially modified gaussian evaluated at xx
Exponentially Modified Gaussian (EMG) minpack.lm objective function, calculates residuals using the EMG Peak Shape
emgGaussian_minpack.lm_objectiveFun(params, observed, xx)emgGaussian_minpack.lm_objectiveFun(params, observed, xx)
params |
(list) exponentially modified gaussian parameters
( |
observed |
(numeric) observed y value at xx |
xx |
(numeric) value at which to evalute the exponentially modified gaussian |
difference between observed and expected exponentially modified gaussian value evaluated at xx
Extract all signal from multiple defined mz rt window from raw data and returns a data.frame. If no rt-mz window is provided, all signal in the raw data file are returned
extractSignalRawData(rawSpec, rt, mz, msLevel = 1L, verbose = TRUE)extractSignalRawData(rawSpec, rt, mz, msLevel = 1L, verbose = TRUE)
rawSpec |
|
rt |
(numeric(2) or two-column matrix) the lower and upper retention time range from which the data should be extracted. If a matrix is passed, each row corresponds to a different window. If not provided, the full retention time range will be extracted. |
mz |
(numeric(2) or two-column matrix) the lower and upper mass range from which the data should be extracted. If a matrix is passed, each row corresponds to a different window. If not provided, the full mass range will be extracted. |
msLevel |
(int) the MS level at which the data should be extracted (default to MS level 1) |
verbose |
(bool) If TRUE message progress and warnings |
## Examples cannot be computed as the function is not exported: ## Use a file form the faahKO package and extract data from a region of ## interest library(faahKO) rawSpec <- MSnbase::readMSData( system.file('cdf/KO/ko15.CDF',package='faahKO'), centroided=TRUE, mode='onDisk') dataPoints <- extractSignalRawData(rawSpec, rt = c(3290., 3410.), mz = c(522.194778, 522.205222), verbose=TRUE) # Reading data from 1 windows
dataPoints # [[1]] # rt mz int # 1 3290.115 522.2 1824 # 2 3291.680 522.2 1734 # 3 3293.245 522.2 1572 # 4 3294.809 522.2 1440 # 5 3299.504 522.2 1008 # 6 3301.069 522.2 871 # 7 3302.634 522.2 786 # 8 3304.199 522.2 802 # 9 3305.764 522.2 834 # 10 3307.329 522.2 839 # 11 3315.154 522.2 2187 # 12 3316.719 522.2 3534 # 13 3318.284 522.2 6338 # 14 3319.849 522.2 11718 # 15 3321.414 522.2 21744 # 16 3322.979 522.2 37872 # 17 3324.544 522.2 62424 # 18 3326.109 522.2 98408 # 19 3327.673 522.2 152896 # 20 3329.238 522.2 225984 # 21 3330.803 522.2 308672 # 22 3332.368 522.2 399360 # 23 3333.933 522.2 504000 # 24 3335.498 522.2 614656 # 25 3337.063 522.2 711872 # 26 3338.628 522.2 784704 # 27 3340.193 522.2 836608 # 28 3341.758 522.2 866304 # 29 3343.323 522.2 882304 # 30 3344.888 522.2 889280 # 31 3346.453 522.2 888256 # 32 3348.018 522.2 866816 # 33 3349.583 522.2 827392 # 34 3351.148 522.2 777728 # 35 3352.713 522.2 727040 # 36 3354.278 522.2 678464 # 37 3355.843 522.2 629120 # 38 3357.408 522.2 578048 # 39 3358.973 522.2 524288 # 40 3360.538 522.2 471040 # 41 3362.102 522.2 416320 # 42 3363.667 522.2 360064 # 43 3365.232 522.2 302400 # 44 3366.797 522.2 249152 # 45 3368.362 522.2 202560 # 46 3369.927 522.2 161024 # 47 3371.492 522.2 123520 # 48 3373.057 522.2 93160 # 49 3374.622 522.2 71856 # 50 3376.187 522.2 58392 # 51 3377.752 522.2 51072 # 52 3379.317 522.2 48376 # 53 3380.882 522.2 49168 # 54 3382.447 522.2 53120 # 55 3384.012 522.2 62488 # 56 3385.577 522.2 78680 # 57 3387.142 522.2 102840 # 58 3388.707 522.2 134656 # 59 3390.272 522.2 173440 # 60 3391.837 522.2 217088 # 61 3393.402 522.2 268864 # 62 3394.966 522.2 330496 # 63 3396.531 522.2 395776 # 64 3398.096 522.2 453376 # 65 3399.661 522.2 499072 # 66 3401.226 522.2 537024 # 67 3402.791 522.2 570304 # 68 3404.356 522.2 592512 # 69 3405.921 522.2 598912 # 70 3407.486 522.2 595008 # 71 3409.051 522.2 588416
a list (one entry per window) of data.frame with signal as row and retention time ('rt'), mass ('mz') and intensity ('int) as columns.
filename accessor by spliting filepath
## S4 method for signature 'peakPantheRAnnotation' filename(object)## S4 method for signature 'peakPantheRAnnotation' filename(object)
object |
peakPantheRAnnotation |
(str) filename
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) filename(annotation) # [1] 'ko15' 'ko16' 'ko18' }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) filename(annotation) # [1] 'ko15' 'ko16' 'ko18' }
filepath accessor
## S4 method for signature 'peakPantheRAnnotation' filepath(object)## S4 method for signature 'peakPantheRAnnotation' filepath(object)
object |
peakPantheRAnnotation |
(str) A character vector of file paths, of length number of spectra files
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) filepath(annotation) # [1] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF' # [2] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF' # [3] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF' }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) filepath(annotation) # [1] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF' # [2] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF' # [3] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF' }
For each ROI, fit a curve and integrate the largest feature in
the box. Each entry in ROIsDataPoints must match the corresponding row
in ROI. The curve shape to employ for fitting can be changed with
curveModel while fitting parameters can be changed with params
(list with one param per ROI window). rtMin and rtMax are
established at 0.5
outward (the window is the ROI width); if after 8 iterations rtMin or
rtMax is not found, NA is returned and the peak fit rejected.
peakArea is calculated from rtMin to rtMax.
peakAreaRaw is calculated from rtMin to rtMax but
using the raw data points instead of the modelled line-shape.
mz is the weighted (by intensity)
average mz of datapoints falling into the rtMin to rtMax
range, mzMin and mzMax are the
minimum and maxmimum mass in these range. If rtMin or rtMax
falls outside of ROI (extracted scans), mzMin or mzMax are
returned as the input ROI limits and mz is an approximation on the
datapoints available (if no scan of the ROI fall between rtMin/rtMax, mz
would be NA, the peak is rejected). If any of the two following ratio are
superior to maxApexResidualRatio, the fit is rejected: 1) ratio of fit
residuals at the apex (predicted apex fit intensity vs measured apex
intensity: fit overshoots the apex), 2) ratio of predicted apex fit intensity
vs maximum measured peak intensity (fit misses the real apex in the peak).
findTargetFeatures( ROIsDataPoints, ROI, curveModel = "skewedGaussian", params = "guess", sampling = 250, maxApexResidualRatio = 0.2, verbose = FALSE, ... )findTargetFeatures( ROIsDataPoints, ROI, curveModel = "skewedGaussian", params = "guess", sampling = 250, maxApexResidualRatio = 0.2, verbose = FALSE, ... )
ROIsDataPoints |
(list) A list (one entry per ROI window) of data.frame with signal as row and retention time ('rt'), mass ('mz') and intensity ('int) as columns. Must match each row of ROI. |
ROI |
(data.frame) A data.frame of compounds to target as rows. Columns:
|
curveModel |
(str) Name of the curve model to fit (currently
|
params |
(list or str) Either 'guess' for automated parametrisation or list (one per ROI windows) of 'guess' or list of curve fit parameters |
sampling |
(int) Number of points to employ when subsampling the fittedCurve (rt, rtMin, rtMax, integral calculation) |
maxApexResidualRatio |
(float) Ratio of maximum allowed fit residual at the peak apex, compared to the fit max intensity. (e.g. 0.2 for a maximum residual of 20% of apex intensity) |
verbose |
(bool) If TRUE message the time taken and number of features found |
... |
Passes arguments to |
## Examples cannot be computed as the function is not exported: ## Load data library(faahKO) library(MSnbase) netcdfFilePath <- system.file('cdf/KO/ko15.CDF', package = 'faahKO') raw_data <- MSnbase::readMSData(netcdfFilePath,centroided=TRUE,mode='onDisk')
## targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin', 'mz','mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,3:8] <- vapply(targetFeatTable[,3:8], as.numeric, FUN.VALUE=numeric(2))
ROIsPt <- extractSignalRawData(raw_data, rt=targetFeatTable[,c('rtMin','rtMax')], mz=targetFeatTable[,c('mzMin','mzMax')], verbose=TRUE) # Reading data from 2 windows
foundPeaks <- findTargetFeatures(ROIsPt, targetFeatTable, verbose=TRUE) # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, # approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #1 # Found 2/2 features in 0.07 secs
foundPeaks # $peakTable # found rtMin rt rtMax mzMin mz mzMax peakArea # 1 TRUE 3309.759 3346.828 3385.410 522.1948 522.2 522.2052 26133727 # 2 TRUE 3345.377 3386.529 3428.279 496.2000 496.2 496.2000 35472141 # peakAreaRaw maxIntMeasured maxIntPredicted # 1 26071378 889280 901015.8 # 2 36498367 1128960 1113576.7 # # $curveFit # $curveFit[[1]] # $amplitude # [1] 162404.8 # # $center # [1] 3341.888 # # $sigma # [1] 0.07878613 # # $gamma # [1] 0.00183361 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit' # # $curveFit[[2]] # $amplitude # [1] 199249.1 # # $center # [1] 3382.577 # # $sigma # [1] 0.07490442 # # $gamma # [1] 0.00114719 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit'
A list: list()$peakTable (data.frame) with targeted
features as rows and peak measures as columns (see Details),
list()$curveFit (list) a list of peakPantheR_curveFit or
NA for each ROI.
The returned data.frame is structured as follow:
| found | was the peak found |
| rt | retention time of peak apex (sec) |
| rtMin | leading edge of peak retention time (sec) determined at 0.5% of apex intensity |
| rtMax | trailing edge of peak retention time (sec) determined at 0.5% of apex intensity |
| mz | weighted (by intensity) mean of peak m/z across scans |
| mzMin | m/z peak minimum (between rtMin, rtMax) |
| mzMax | m/z peak maximum (between rtMin, rtMax) |
| peakArea | integrated peak area |
| peakAreaRaw | integrated peak area from raw data points |
| maxIntMeasured | maximum peak intensity in raw data |
| maxIntPredicted | maximum peak intensity based on curve fit (at apex) |
FIR accessor returns targetFeatTable with cpdID, cpdName added
## S4 method for signature 'peakPantheRAnnotation' FIR(object)## S4 method for signature 'peakPantheRAnnotation' FIR(object)
object |
peakPantheRAnnotation |
(data.frame) target feature table with compounds as row and FIR parameters as columns
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation FIR(annotation) # rtMin rtMax mzMin mzMax cpdID cpdName # 1 NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA ID-2 Cpd 2 }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation FIR(annotation) # rtMin rtMax mzMin mzMax cpdID cpdName # 1 NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA ID-2 Cpd 2 }
Fit different curve models using minpack. Fitting
parameters can be passed or guessed.
fitCurve(x, y, curveModel = "skewedGaussian", params = "guess")fitCurve(x, y, curveModel = "skewedGaussian", params = "guess")
x |
(numeric) x values (e.g. retention time) |
y |
(numeric) y observed values (e.g. spectra intensity) |
curveModel |
(str) name of the curve model to fit (currently
|
params |
(list or str) either 'guess' for automated parametrisation or
list of initial parameters ( |
## Examples cannot be computed as the function is not exported: ## x is retention time, y corresponding intensity input_x <- c(3362.102, 3363.667, 3365.232, 3366.797, 3368.362, 3369.927, 3371.492, 3373.057, 3374.622, 3376.187, 3377.752, 3379.317, 3380.882, 3382.447, 3384.012, 3385.577, 3387.142, 3388.707, 3390.272, 3391.837, 3393.402, 3394.966, 3396.531, 3398.096, 3399.661, 3401.226, 3402.791, 3404.356, 3405.921, 3407.486, 3409.051) input_y <- c(51048, 81568, 138288, 233920, 376448, 557288, 753216, 938048, 1091840, 1196992, 1261056, 1308992, 1362752, 1406592, 1431360, 1432896, 1407808, 1345344, 1268480, 1198592, 1126848, 1036544, 937600, 849792, 771456, 692416, 614528, 546088, 492752, 446464, 400632)
## Fit fitted_curve <- fitCurve(input_x, input_y, curveModel='skewedGaussian', params='guess')
## Returns the optimal fitting parameters fitted_curve # # $amplitude # [1] 275371.1 # # $center # [1] 3382.577 # # $sigma # [1] 0.07904697 # # $gamma # [1] 0.001147647 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit'
A 'peakPantheR_curveFit': a list of fitted curve parameters,
fitStatus from nls.lm$info and curve shape name
curveModel. fitStatus=0 unsuccessful completion: improper input
parameters, fitStatus=1 successful completion: first convergence test
is successful, fitStatus=2 successful completion: second convergence
test is successful, fitStatus=3 successful completion: both
convergence test are successful, fitStatus=4 questionable completion:
third convergence test is successful but should be carefully examined
(maximizers and saddle points might satisfy), fitStatus=5 unsuccessful
completion: excessive number of function evaluations/iterations
Implementation of the gaussian error function
gaussian_cerf(x)gaussian_cerf(x)
x |
(numeric) value at which to evaluate the gaussian error function |
Value of the gaussian error function evaluated at x
Implementation of the gaussian error function
gaussian_erf(x)gaussian_erf(x)
x |
(numeric) value at which to evaluate the gaussian error function |
Value of the gaussian error function evaluated at x
On the input raw data, aggregate intensites across the mz range
at each retention time to generate an ion chromatogram: sum for
EIC/TIC, max, min or mean. The number of data points
returned correspond to the number of unique scans/retention time measurements
in the input data
generateIonChromatogram(ROIDataPoint, aggregationFunction = "sum")generateIonChromatogram(ROIDataPoint, aggregationFunction = "sum")
ROIDataPoint |
(data.frame) retention time 'rt', mass 'mz' and intensity 'int' (as column) of each raw data points (as row) to use for the ion chromatogram |
aggregationFunction |
(str) Function to use in order to aggregate
intensities across mz in each scan. One of |
## Examples cannot be computed as the function is not exported: ## Input data points in_rt <- c(3362.102, 3362.102, 3363.667, 3363.667, 3365.232, 3365.232, 3366.797, 3366.797, 3368.362, 3368.362) in_mz <- c(496.2, 497.2, 496.2, 497.2, 496.2, 497.2, 496.2, 497.2, 496.2, 497.2) in_int <- c(39616, 11432, 63344, 18224, 107352, 30936, 182144, 51776, 295232, 81216) input_ROIDataPoints <- data.frame(rt=in_rt, mz=in_mz, int=in_int)
## Aggregate mz to generate EIC EIC <- generateIonChromatogram(input_ROIDataPoints,aggregationFunction='sum') EIC # rt int # 1 3362.102 51048 # 2 3363.667 81568 # 3 3365.232 138288 # 4 3366.797 233920 # 5 3368.362 376448
A data.frame of retention time 'rt' and aggregated intensities 'int'
Extract acquisition date (''startTimeStamp'“) from a mzML file.
In case of failure (or the file is not a mzML) returns NULL
getAcquisitionDatemzML(mzMLPath, verbose = TRUE)getAcquisitionDatemzML(mzMLPath, verbose = TRUE)
mzMLPath |
(str) path to mzML raw data file |
verbose |
(bool) if TRUE message progress |
POSIXct or NA
Calculate the ppm error, retention time deviation, tailing factor and asymmetry factor for each measured feature.
getTargetFeatureStatistic( fittedCurves, targetFeatTable, foundPeakTable, verbose = FALSE )getTargetFeatureStatistic( fittedCurves, targetFeatTable, foundPeakTable, verbose = FALSE )
fittedCurves |
(list) A list (one entry per ROI window) of
|
targetFeatTable |
a |
foundPeakTable |
a |
verbose |
(bool) if TRUE message when NA scans are removed |
## Examples cannot be computed as the function is not exported: # fittedCurve cFit1 <- list(amplitude=162404.8057918259, center=3341.888, sigma=0.078786133031045896, gamma=0.0018336101984172684, fitStatus=2, curveModel='skewedGaussian') class(cFit1) <- 'peakPantheR_curveFit' cFit2 <- list(amplitude=199249.10572753669, center=3382.577, sigma=0.074904415304607966, gamma=0.0011471899372353885, fitStatus=2, curveModel='skewedGaussian') class(cFit2) <- 'peakPantheR_curveFit' input_fitCurves <- list(cFit1, cFit2)
# ROI input_ROI <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) input_ROI[1,] <- c('ID-1', 'testCpd 1', 3310., 3344.88, 3390., 522.19, 522.2, 522.21) input_ROI[2,] <- c('ID-2', 'testCpd 2', 3280., 3385.58, 3440., 496.19, 496.2, 496.21) input_ROI[,3:8] <- vapply(input_ROI[,3:8], as.numeric, FUN.VALUE=numeric(2))
# foundPeakTable input_foundPeakTable <- data.frame(matrix(vector(), 2, 10, dimnames=list(c(), c('found', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax','peakArea','maxIntMeasured','maxIntPredicted'))), stringsAsFactors=FALSE) input_foundPeakTable[1,] <- c(TRUE, 3309.758, 3346.827, 3385.410, 522.19, 522.2, 522.21, 26133726, 889280, 901015) input_foundPeakTable[2,] <- c(TRUE, 3345.376, 3386.529, 3428.279, 496.19, 496.2, 496.21, 35472141, 1128960, 1113576) input_foundPeakTable[,1] <- vapply(input_foundPeakTable[,c(1)], as.logical, FUN.VALUE=logical(1))
# Run peak statistics peakStatistics <- getTargetFeatureStatistic(input_fitCurves, input_ROI, input_foundPeakTable) peakStatistics # found rtMin rt rtMax mzMin mz mzMax peakArea # 1 TRUE 3309.758 3346.827 3385.410 522.19 522.2 522.21 26133726 # 2 TRUE 3345.376 3386.529 3428.279 496.19 496.2 496.21 35472141 # maxIntMeasured maxIntPredicted ppm_error rt_dev_sec tailingFactor # 1 889280 901015 0 1.947 1.015385 # 2 1128960 1113576 0 0.949 1.005372 # asymmetryFactor # 1 1.026886 # 2 1.009304
A data.frame with measured compounds as rows and measurements
and properties as columns (see Details).
The returned data.frame is structured as follow:
| found | was the peak found |
| rt | retention time of peak apex (sec) |
| rtMin | leading edge of peak retention time (sec) determined at 0.5% of apex intensity |
| rtMax | trailing edge of peak retention time (sec) determined at 0.5% of apex intensity |
| mz | weighted (by intensity) mean of peak m/z across scans |
| mzMin | m/z peak minimum (between rtMin, rtMax) |
| mzMax | m/z peak maximum (between rtMin, rtMax) |
| peakArea | integrated peak area |
| peakAreaRaw | integrated peak area from raw data points |
| maxIntMeasured | maximum peak intensity in raw data |
| maxIntPredicted | maximum peak intensity based on curve fit |
| ppm_error | difference in ppm between the expected and measured m/z |
| rt_dev_sec | difference in seconds between the expected and measured rt |
| tailingFactor | the tailing factor is a measure of peak tailing. It is defined as the distance from the front slope of the peak to the back slope divided by twice the distance from the center line of the peak to the front slope, with all measurements made at 5% of the maximum peak height. The tailing factor of a peak will typically be similar to the asymmetry factor for the same peak, but the two values cannot be directly converted |
| asymmetryFactor | the asymmetry factor is a measure of peak tailing. It is defined as the distance from the center line of the peak to the back slope divided by the distance from the center line of the peak to the front slope, with all measurements made at 10% of the maximum peak height. The asymmetry factor of a peak will typically be similar to the tailing factor for the same peak, but the two values cannot be directly converted |
Fully initialise a peakPantheRAnnotation using the target files path,
CSV parameter path and metadata.
initialise_annotation_from_files_UI_helper( CSVParamPath, spectraPaths, cpdMetadataPath = NULL, spectraMetadata = NULL, verbose = TRUE )initialise_annotation_from_files_UI_helper( CSVParamPath, spectraPaths, cpdMetadataPath = NULL, spectraMetadata = NULL, verbose = TRUE )
CSVParamPath |
(str) Path to a CSV file of fit parameters |
spectraPaths |
(str) character vector of spectra file paths, to set samples to process |
cpdMetadataPath |
NULL or path to a csv of compound metadata, with compounds as row and metadata as columns |
spectraMetadata |
NULL or DataFrame of sample metadata, with samples as row and metadata as columns |
verbose |
(bool) If TRUE message progress |
(peakPantheRAnnotation) Object initialised with ROI, uROI and FIR read from the CSV file
## Input data input_CSV <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X','ROI_rt', 'ROI_mz', 'ROI_rtMin', 'ROI_rtMax','ROI_mzMin','ROI_mzMax', 'X','uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax')))) input_CSV[1,] <- c('ID-1', 'Cpd 1', '|', 1., 2., 3., 4., 5., 6., '|', 7., 8., 9., 10., 11., 12., '|', 13., 14., 15., 16.) input_CSV[2,] <- c('ID-2', 'Cpd 2', '|', 17., 18., 19., 20., 21., 22., '|', 23., 24., 25., 26., 27., 28., '|', 29., 30., 31., 32.) input_CSV[,-c(1,2,3,10,17)] <- vapply(input_CSV[,-c(1,2,3,10,17)], as.numeric, FUN.VALUE=numeric(2)) input_spectraPaths <- c('./path/file1', './path/file2', './path/file3') # temporary file location savePath1 <- tempfile(pattern='file', tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_CSV, file=savePath1, row.names=FALSE) # Load parameters from CSV loadedAnnotation <- initialise_annotation_from_files_UI_helper(savePath1, input_spectraPaths, verbose=TRUE) # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated## Input data input_CSV <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X','ROI_rt', 'ROI_mz', 'ROI_rtMin', 'ROI_rtMax','ROI_mzMin','ROI_mzMax', 'X','uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax')))) input_CSV[1,] <- c('ID-1', 'Cpd 1', '|', 1., 2., 3., 4., 5., 6., '|', 7., 8., 9., 10., 11., 12., '|', 13., 14., 15., 16.) input_CSV[2,] <- c('ID-2', 'Cpd 2', '|', 17., 18., 19., 20., 21., 22., '|', 23., 24., 25., 26., 27., 28., '|', 29., 30., 31., 32.) input_CSV[,-c(1,2,3,10,17)] <- vapply(input_CSV[,-c(1,2,3,10,17)], as.numeric, FUN.VALUE=numeric(2)) input_spectraPaths <- c('./path/file1', './path/file2', './path/file3') # temporary file location savePath1 <- tempfile(pattern='file', tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_CSV, file=savePath1, row.names=FALSE) # Load parameters from CSV loadedAnnotation <- initialise_annotation_from_files_UI_helper(savePath1, input_spectraPaths, verbose=TRUE) # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated
Integrate region defined in FIR if a feature is not found
integrateFIR(rawSpec, FIR, foundPeakTable, verbose = TRUE)integrateFIR(rawSpec, FIR, foundPeakTable, verbose = TRUE)
rawSpec |
|
FIR |
(data.frame) Fallback Integration Regions (FIR) to integrate when
a feature is not found. Compounds as row are identical to the targeted
features, columns are |
foundPeakTable |
a |
verbose |
(bool) if TRUE message progress |
an updated foundPeakTable with FIR integration values
Check if object is of class peakPantheR_curveFit
is.peakPantheR_curveFit(x)is.peakPantheR_curveFit(x)
x |
object to test |
(bool) TRUE or FALSE
isAnnotated accessor
## S4 method for signature 'peakPantheRAnnotation' isAnnotated(object)## S4 method for signature 'peakPantheRAnnotation' isAnnotated(object)
object |
peakPantheRAnnotation |
(bool) flag if the annotation has taken place
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) isAnnotated(annotation) # [1] FALSE }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) isAnnotated(annotation) # [1] FALSE }
Load a .RData file (check it exists) and that a peakPantheRAnnotation named "annotationObject" is present. Returns the annotation if everything is valid
load_annotation_from_file_UI_helper(annotationPath)load_annotation_from_file_UI_helper(annotationPath)
annotationPath |
(str) Path to a RData file containing a peakPantheRAnnotation names 'annotationObject' |
(peakPantheRAnnotation) Object loaded from file
## Initialise a peakPantheRAnnotation object with 3 samples and 2 compounds ## Inputs # spectraPaths spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotationObject <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # save annotation to disk annotPath <- tempfile(pattern="file", tmpdir=tempdir(), fileext='.RData') save(annotationObject, file=annotPath, compress=TRUE) # Load annotation load_annotation_from_file_UI_helper(annotationPath = annotPath) # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated## Initialise a peakPantheRAnnotation object with 3 samples and 2 compounds ## Inputs # spectraPaths spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotationObject <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # save annotation to disk annotPath <- tempfile(pattern="file", tmpdir=tempdir(), fileext='.RData') save(annotationObject, file=annotPath, compress=TRUE) # Load annotation load_annotation_from_file_UI_helper(annotationPath = annotPath) # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated
nbCompounds accessor established on cpdID
## S4 method for signature 'peakPantheRAnnotation' nbCompounds(object)## S4 method for signature 'peakPantheRAnnotation' nbCompounds(object)
object |
peakPantheRAnnotation |
(int) number of samples
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) nbCompounds(annotation) # [1] 2 }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) nbCompounds(annotation) # [1] 2 }
nbSamples accessor established on filepath
## S4 method for signature 'peakPantheRAnnotation' nbSamples(object)## S4 method for signature 'peakPantheRAnnotation' nbSamples(object)
object |
peakPantheRAnnotation |
(int) number of samples
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) nbSamples(annotation) # [1] 3 }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) nbSamples(annotation) # [1] 3 }
Save to disk the annotation parameters as CSV (as generated by
outputAnnotationParamsCSV()) and a diagnostic plot per fitted compound
(as generated by annotationDiagnosticMultiplot()) if savePlots
is TRUE
## S4 method for signature 'peakPantheRAnnotation' outputAnnotationDiagnostic( object, saveFolder, savePlots = TRUE, sampleColour = NULL, verbose = TRUE, ncores = 0, svgPlot = FALSE, ... )## S4 method for signature 'peakPantheRAnnotation' outputAnnotationDiagnostic( object, saveFolder, savePlots = TRUE, sampleColour = NULL, verbose = TRUE, ncores = 0, svgPlot = FALSE, ... )
object |
(peakPantheRAnnotation) Annotated peakPantheRAnnotation object |
saveFolder |
(str) Path of folder where annotationParameters_summary.csv and plots will be saved |
savePlots |
(bool) If TRUE save a diagnostic plot for each compound |
sampleColour |
(str) NULL or vector colour for each sample |
verbose |
(bool) If TRUE message progress |
ncores |
(int) Number of cores to use to save plots in parallel |
svgPlot |
(bool) If TRUE save plots as 'svg', otherwise as 'png' |
... |
Additional parameters for plotting i.e. |
None
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Calculate annotation annotation <- peakPantheR_parallelAnnotation(emptyAnnotation, ncores=0, getAcquTime=FALSE, verbose=FALSE)$annotation # temporary location savePath1 <- tempdir() outputAnnotationDiagnostic(annotation, saveFolder=savePath1, savePlots=FALSE, verbose=TRUE) }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Calculate annotation annotation <- peakPantheR_parallelAnnotation(emptyAnnotation, ncores=0, getAcquTime=FALSE, verbose=FALSE)$annotation # temporary location savePath1 <- tempdir() outputAnnotationDiagnostic(annotation, saveFolder=savePath1, savePlots=FALSE, verbose=TRUE) }
Return a table with features as rows and all feature metadata as columns
outputAnnotationFeatureMetadata_UI_helper(annot)outputAnnotationFeatureMetadata_UI_helper(annot)
annot |
(peakPantheRAnnotation) Annotation object |
(data.frame) Features metadata
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # featureMetadata of an empty annotation outputAnnotationFeatureMetadata_UI_helper(emptyAnnotation) # data frame with 0 columns and 2 rows## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # featureMetadata of an empty annotation outputAnnotationFeatureMetadata_UI_helper(emptyAnnotation) # data frame with 0 columns and 2 rows
Save annotation parameters (ROI, uROI and FIR) to disk as a CSV file for editing
## S4 method for signature 'peakPantheRAnnotation' outputAnnotationParamsCSV( object, saveFolder, verbose, noSave )## S4 method for signature 'peakPantheRAnnotation' outputAnnotationParamsCSV( object, saveFolder, verbose, noSave )
object |
(peakPantheRAnnotation) Annotated peakPantheRAnnotation object |
saveFolder |
(str) Path of folder where annotationParameters_summary.csv will be saved |
verbose |
(bool) If TRUE message progress |
noSave |
(bool) If TRUE the resulting table will be returned without saving to disk |
None
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # temporary file savePath <- tempdir() # statistics of an empty annotation outputAnnotationParamsCSV(emptyAnnotation, saveFolder=savePath, verbose=TRUE)## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # temporary file savePath <- tempdir() # statistics of an empty annotation outputAnnotationParamsCSV(emptyAnnotation, saveFolder=savePath, verbose=TRUE)
Save to disk all annotation results as
annotationName_ ... .csv files: compound metadata (cpdMetadata,
cpdID, cpdName) and spectra metadata (spectraMetadata,
acquisitionTime, TIC), summary of fit (ratio of peaks found:
ratio_peaks_found, ratio of peaks filled: ratio_peaks_filled,
mean ppm_error: ppm_error, mean rt_dev_sec: rt_dev_sec), and a
file for each column of peakTables (with samples as rows and compounds
as columns)
## S4 method for signature 'peakPantheRAnnotation' outputAnnotationResult( object, saveFolder, annotationName = "annotationResult", verbose = TRUE )## S4 method for signature 'peakPantheRAnnotation' outputAnnotationResult( object, saveFolder, annotationName = "annotationResult", verbose = TRUE )
object |
(peakPantheRAnnotation) Annotated peakPantheRAnnotation object |
saveFolder |
(str) Path of folder where the annotation result csv will be saved |
annotationName |
(str) name of annotation to use in the saved csv |
verbose |
(bool) If TRUE message progress |
None
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Calculate annotation annotation <- peakPantheR_parallelAnnotation(emptyAnnotation, ncores=0, getAcquTime=FALSE, verbose=FALSE)$annotation # temporary location savePath1 <- tempdir() outputAnnotationResult(annotation, saveFolder=savePath1, annotationName='testProject', verbose=TRUE) }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # Calculate annotation annotation <- peakPantheR_parallelAnnotation(emptyAnnotation, ncores=0, getAcquTime=FALSE, verbose=FALSE)$annotation # temporary location savePath1 <- tempdir() outputAnnotationResult(annotation, saveFolder=savePath1, annotationName='testProject', verbose=TRUE) }
Return a table with spectra as rows and filepath and all spectra metadata columns
outputAnnotationSpectraMetadata_UI_helper(annot)outputAnnotationSpectraMetadata_UI_helper(annot)
annot |
(peakPantheRAnnotation) Annotation object |
(data.frame) Spectra paths and metadata
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # spectraMetada of an empty annotation outputAnnotationSpectraMetadata_UI_helper(emptyAnnotation) # filepath # 1 ./path/file1 # 2 ./path/file2 # 3 ./path/file3## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # spectraMetada of an empty annotation outputAnnotationSpectraMetadata_UI_helper(emptyAnnotation) # filepath # 1 ./path/file1 # 2 ./path/file2 # 3 ./path/file3
peakFit accessor
## S4 method for signature 'peakPantheRAnnotation' peakFit(object)## S4 method for signature 'peakPantheRAnnotation' peakFit(object)
object |
peakPantheRAnnotation |
A list of length number of spectra files. Each list element is a
curveFit list of peakPantheR_curveFit or NA for each ROI
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation peakFit(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation peakFit(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }
peakPantheR detects, integrates and reports pre-defined features in mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files.
The main functions of peakPantheR are
peakPantheR_singleFileSearch for realtime annotation, and
peakPantheR_parallelAnnotation for parallel annotation. The
peakPantheRAnnotation object stores parallel annotation results, while
reporting functions help assess the quality of annotation and update
fitting parameters. Refer to the vignettes for graphical user interface and
command line tutorials.
Maintainer: Arnaud Wolfer [email protected] (ORCID)
Authors:
Goncalo Correia [email protected] (ORCID)
Other contributors:
Jake Pearce [email protected] [contributor]
Caroline Sands [email protected] [contributor]
Useful links:
Report bugs at https://github.com/phenomecentre/peakPantheR/issues/new
Correct targeted features retention time using the RT and RT deviation of previously fitted compounds. The 'method' and 'params' are used to select and parametrise the retention time correction method employed. When 'robust' is set to TRUE, the RANSAC algorithm is used to automatically flag outliers and robustify the correction function fitting.
peakPantheR_applyRTCorrection( targetFeatTable, referenceTable, method = "polynomial", params = list(polynomialOrder = 3), robust = TRUE )peakPantheR_applyRTCorrection( targetFeatTable, referenceTable, method = "polynomial", params = list(polynomialOrder = 3), robust = TRUE )
targetFeatTable |
a |
referenceTable |
a |
method |
(str) name of RT correction method to use (currently
|
params |
(list) list of parameters to pass to
the each correction method.
Currently allowed inputs are |
robust |
(bool) whether to use the RANSAC algorithm to flag and ignore outliers during retention time correction |
a targetFeatTable with corrected RT
Initialise a new peakPantheRAnnotation object after
loading ROI, uROI and FIR parameters from CSV. spectraPaths,
spectraMetadata or cpdMetadata are not initialised and will
need to be filled before annotation. useUROI and useFIR are set
to FALSE and will need to be set accordingly. uROIExist is
established depending on the uROI columns present in the CSV and will be set
to TRUE only if no NA are present
peakPantheR_loadAnnotationParamsCSV(CSVParamPath, verbose = TRUE)peakPantheR_loadAnnotationParamsCSV(CSVParamPath, verbose = TRUE)
CSVParamPath |
(str) Path to a CSV file of fit parameters (e.g. as
saved by |
verbose |
(bool) If TRUE message progress |
(peakPantheRAnnotation) Object initialised with ROI, uROI and FIR read from the CSV file
## Input data input_CSV <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X','ROI_rt', 'ROI_mz', 'ROI_rtMin', 'ROI_rtMax','ROI_mzMin','ROI_mzMax', 'X','uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax')))) input_CSV[1,] <- c('ID-1', 'Cpd 1', '|', 1., 2., 3., 4., 5., 6., '|', 7., 8., 9., 10., 11., 12., '|', 13., 14., 15., 16.) input_CSV[2,] <- c('ID-2', 'Cpd 2', '|', 17., 18., 19., 20., 21., 22., '|', 23., 24., 25., 26., 27., 28., '|', 29., 30., 31., 32.) input_CSV[,-c(1,2,3,10,17)] <- vapply(input_CSV[,-c(1,2,3,10,17)], as.numeric, FUN.VALUE=numeric(2)) # temporary file location savePath1 <- tempfile(pattern='file', tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_CSV, file=savePath1, row.names=FALSE) # Load parameters from CSV loadedAnnotation <- peakPantheR_loadAnnotationParamsCSV(savePath1, verbose=TRUE) # uROIExist set to TRUE # New peakPantheRAnnotation object initialised for 2 compounds # An object of class peakPantheRAnnotation # 2 compounds in 0 samples. # updated ROI exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated## Input data input_CSV <- data.frame(matrix(nrow=2,ncol=21,dimnames=list(c(), c('cpdID', 'cpdName', 'X','ROI_rt', 'ROI_mz', 'ROI_rtMin', 'ROI_rtMax','ROI_mzMin','ROI_mzMax', 'X','uROI_rtMin', 'uROI_rtMax', 'uROI_mzMin', 'uROI_mzMax', 'uROI_rt', 'uROI_mz', 'X', 'FIR_rtMin', 'FIR_rtMax', 'FIR_mzMin', 'FIR_mzMax')))) input_CSV[1,] <- c('ID-1', 'Cpd 1', '|', 1., 2., 3., 4., 5., 6., '|', 7., 8., 9., 10., 11., 12., '|', 13., 14., 15., 16.) input_CSV[2,] <- c('ID-2', 'Cpd 2', '|', 17., 18., 19., 20., 21., 22., '|', 23., 24., 25., 26., 27., 28., '|', 29., 30., 31., 32.) input_CSV[,-c(1,2,3,10,17)] <- vapply(input_CSV[,-c(1,2,3,10,17)], as.numeric, FUN.VALUE=numeric(2)) # temporary file location savePath1 <- tempfile(pattern='file', tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_CSV, file=savePath1, row.names=FALSE) # Load parameters from CSV loadedAnnotation <- peakPantheR_loadAnnotationParamsCSV(savePath1, verbose=TRUE) # uROIExist set to TRUE # New peakPantheRAnnotation object initialised for 2 compounds # An object of class peakPantheRAnnotation # 2 compounds in 0 samples. # updated ROI exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated
Integrate all target features in all files defined in the
initialised input object and store results. The use of updated ROI and the
integration of FIR are controled by the input object slots useUROI and
useFIR. Files are processed in parallel using
peakPantheR_singleFileSearch; ncores controls the number of
cores used for parallelisation, with ncores=0 corresponding to serial
processing. If the processing of a file fails (file does not exist or error
during execution) the sample is removed from the outputed object.
peakPantheR_parallelAnnotation( object, ncores = 0, getAcquTime = TRUE, resetWorkers = 1, centroided = TRUE, curveModel = "skewedGaussian", verbose = TRUE, ... )peakPantheR_parallelAnnotation( object, ncores = 0, getAcquTime = TRUE, resetWorkers = 1, centroided = TRUE, curveModel = "skewedGaussian", verbose = TRUE, ... )
object |
(peakPantheRAnnotation) Initialised peakPantheRAnnotation
object defining the samples to process and compounds to target. The slots
|
ncores |
(int) Number of cores to use for parallelisation. Default 0 for no parallelisation. |
getAcquTime |
(bool) If TRUE will extract sample acquisition date-time from the mzML metadata (the additional file access will impact run time) |
resetWorkers |
(int) If 0, the parallel cluster is only initiated once.
If >0 the cluster will be reset (and the memory of each worker freed) once
|
centroided |
(bool) use TRUE if the data is centroided, used by
|
curveModel |
(str) specify the peak-shape model to fit,
by default |
verbose |
(bool) If TRUE message calculation progress, time taken, number of features found (total and matched to targets) and failures |
... |
Passes arguments to |
a list: list()$result (peakPantheRAnnotation) fully
annotated object, list()$failures (list) list of failed samples
and error message
Other peakPantheR:
peakPantheRAnnotation,
peakPantheR_singleFileSearch()
Other parallelAnnotation:
peakPantheRAnnotation,
peakPantheR_singleFileSearch()
if(requireNamespace('faahKO')){ ## Load data library(faahKO) # 3 files input_spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # 4 features input_ROI <- data.frame(matrix(vector(), 4, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) input_ROI[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) input_ROI[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) input_ROI[3,] <- c('ID-3', 'Cpd 3', 3420., 3454.435, 3495., 464.195358, 464.2, 464.204642) input_ROI[4,] <- c('ID-4', 'Cpd 4', 3670., 3701.697, 3745., 536.194638, 536.2, 536.205362) input_ROI[,c(3:8)] <- vapply(input_ROI[,c(3:8)], as.numeric, FUN.VALUE=numeric(4)) # Initialise object initAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_ROI) # to use updated ROI: # uROIExist=TRUE, useUROI=TRUE, uROI=input_uROI # to use FallBack Integration Regions: # useFIR=TRUE, FIR=input_FIR # Run serially result_parallelAnnotation <- peakPantheR_parallelAnnotation(initAnnotation, ncores=0, getAcquTime=FALSE, verbose=TRUE) # Processing 4 compounds in 3 samples: # uROI:\tFALSE # FIR:\tFALSE # ----- ko15 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.24 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #3 # Found 4/4 features in 0.06 secs # Peak statistics done in: 0.02 secs # Feature search done in: 0.76 secs # ----- ko16 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.24 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #2 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #3 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #4 # Found 4/4 features in 0.08 secs # Peak statistics done in: 0 secs # Feature search done in: 0.71 secs # ----- ko18 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.25 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #2 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #4 # Found 4/4 features in 0.06 secs # Peak statistics done in: 0 secs # Feature search done in: 0.71 secs # ---------------- # Parallel annotation done in: 2.18 secs # No failures result_parallelAnnotation$failures result_parallelAnnotation$annotation # An object of class peakPantheRAnnotation # 4 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is annotated }if(requireNamespace('faahKO')){ ## Load data library(faahKO) # 3 files input_spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # 4 features input_ROI <- data.frame(matrix(vector(), 4, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) input_ROI[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) input_ROI[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) input_ROI[3,] <- c('ID-3', 'Cpd 3', 3420., 3454.435, 3495., 464.195358, 464.2, 464.204642) input_ROI[4,] <- c('ID-4', 'Cpd 4', 3670., 3701.697, 3745., 536.194638, 536.2, 536.205362) input_ROI[,c(3:8)] <- vapply(input_ROI[,c(3:8)], as.numeric, FUN.VALUE=numeric(4)) # Initialise object initAnnotation <- peakPantheRAnnotation(spectraPaths=input_spectraPaths, targetFeatTable=input_ROI) # to use updated ROI: # uROIExist=TRUE, useUROI=TRUE, uROI=input_uROI # to use FallBack Integration Regions: # useFIR=TRUE, FIR=input_FIR # Run serially result_parallelAnnotation <- peakPantheR_parallelAnnotation(initAnnotation, ncores=0, getAcquTime=FALSE, verbose=TRUE) # Processing 4 compounds in 3 samples: # uROI:\tFALSE # FIR:\tFALSE # ----- ko15 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.24 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #3 # Found 4/4 features in 0.06 secs # Peak statistics done in: 0.02 secs # Feature search done in: 0.76 secs # ----- ko16 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.24 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #2 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #3 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #4 # Found 4/4 features in 0.08 secs # Peak statistics done in: 0 secs # Feature search done in: 0.71 secs # ----- ko18 ----- # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 4 windows # Data read in: 0.25 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #2 # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #4 # Found 4/4 features in 0.06 secs # Peak statistics done in: 0 secs # Feature search done in: 0.71 secs # ---------------- # Parallel annotation done in: 2.18 secs # No failures result_parallelAnnotation$failures result_parallelAnnotation$annotation # An object of class peakPantheRAnnotation # 4 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is annotated }
plot a ROI across multiple samples (x axis is RT, y axis is intensity). If curveFit is provided, the fitted curve for each sample is added.
peakPantheR_plotEICFit( ROIDataPointSampleList, curveFitSampleList = NULL, rtMin = NULL, rtMax = NULL, sampling = 250, sampleColour = NULL, verbose = TRUE )peakPantheR_plotEICFit( ROIDataPointSampleList, curveFitSampleList = NULL, rtMin = NULL, rtMax = NULL, sampling = 250, sampleColour = NULL, verbose = TRUE )
ROIDataPointSampleList |
(list) list of |
curveFitSampleList |
(list) NULL or a list of
|
rtMin |
(float) NULL or vector of detected peak minimum retention time (in sec) |
rtMax |
(float) NULL or vector of detected peak maximum retention time (in sec) |
sampling |
(int) Number of points to employ when plotting fittedCurve |
sampleColour |
(str) NULL or vector colour for each sample (same length
as |
verbose |
(bool) if TRUE message when NA scans are removed |
Grob (ggplot object)
## Input data # fake sample 1 # ROI data points rt1 <- seq(990, 1010, by=20/250) mz1 <- rep(522., length(rt1)) int1 <- (dnorm(rt1, mean=1000, sd=1.5) * 100) + 1 tmp_DataPoints1 <- data.frame(rt=rt1, mz=mz1, int=int1) # fittedCurve fit1 <- list(amplitude=37.068916502809756, center=999.3734222573454, sigma=0.58493182568124724, gamma=0.090582029276037035, fitStatus=2, curveModel='skewedGaussian') class(fit1) <- 'peakPantheR_curveFit' # fake sample 2 # ROI data points rt2 <- seq(990, 1010, by=20/250) mz2 <- rep(522., length(rt2)) int2 <- (dnorm(rt2, mean=1002, sd=1.5) * 100) + 1 tmp_DataPoints2 <- data.frame(rt=rt2, mz=mz2, int=int2) # fittedCurve fit2 <- list(amplitude=37.073067416755556, center=1001.3736564832565, sigma=0.58496485738212201, gamma=0.090553713725151905, fitStatus=2, curveModel='skewedGaussian') class(fit2) <- 'peakPantheR_curveFit' ## Plot features in 1 sample without colours peakPantheR_plotEICFit(ROIDataPointSampleList=list(tmp_DataPoints1), curveFitSampleList=list(fit1), rtMin=995., rtMax=1005., sampling=250, sampleColour=NULL, verbose=FALSE) ## Plot features in 2 samples with colours peakPantheR_plotEICFit( ROIDataPointSampleList=list(tmp_DataPoints1,tmp_DataPoints2), curveFitSampleList=list(fit1, fit2), rtMin=c(995., 997.), rtMax=c(1005.,1007.), sampling=250, sampleColour=c('blue', 'red'), verbose=FALSE)## Input data # fake sample 1 # ROI data points rt1 <- seq(990, 1010, by=20/250) mz1 <- rep(522., length(rt1)) int1 <- (dnorm(rt1, mean=1000, sd=1.5) * 100) + 1 tmp_DataPoints1 <- data.frame(rt=rt1, mz=mz1, int=int1) # fittedCurve fit1 <- list(amplitude=37.068916502809756, center=999.3734222573454, sigma=0.58493182568124724, gamma=0.090582029276037035, fitStatus=2, curveModel='skewedGaussian') class(fit1) <- 'peakPantheR_curveFit' # fake sample 2 # ROI data points rt2 <- seq(990, 1010, by=20/250) mz2 <- rep(522., length(rt2)) int2 <- (dnorm(rt2, mean=1002, sd=1.5) * 100) + 1 tmp_DataPoints2 <- data.frame(rt=rt2, mz=mz2, int=int2) # fittedCurve fit2 <- list(amplitude=37.073067416755556, center=1001.3736564832565, sigma=0.58496485738212201, gamma=0.090553713725151905, fitStatus=2, curveModel='skewedGaussian') class(fit2) <- 'peakPantheR_curveFit' ## Plot features in 1 sample without colours peakPantheR_plotEICFit(ROIDataPointSampleList=list(tmp_DataPoints1), curveFitSampleList=list(fit1), rtMin=995., rtMax=1005., sampling=250, sampleColour=NULL, verbose=FALSE) ## Plot features in 2 samples with colours peakPantheR_plotEICFit( ROIDataPointSampleList=list(tmp_DataPoints1,tmp_DataPoints2), curveFitSampleList=list(fit1, fit2), rtMin=c(995., 997.), rtMax=c(1005.,1007.), sampling=250, sampleColour=c('blue', 'red'), verbose=FALSE)
For a single ROI, plot the peak value and peakwidth (RT, m/z,
...) of detected peaks across multiple samples, by acquisition time or in
input order. If rotateAxis=FALSE x is run order / plot order, y is the
apexValue / widthMin / widthMax, if
rotateAxis=TRUE x is the measurement values and y the run order.
peakPantheR_plotPeakwidth( apexValue, widthMin = NULL, widthMax = NULL, acquTime = NULL, varName = "variable", sampleColour = NULL, rotateAxis = FALSE, verbose = TRUE )peakPantheR_plotPeakwidth( apexValue, widthMin = NULL, widthMax = NULL, acquTime = NULL, varName = "variable", sampleColour = NULL, rotateAxis = FALSE, verbose = TRUE )
apexValue |
(float) vector of apex value |
widthMin |
(float) vector of detected peak minimum peakwidth value or NULL (if NULL no peakwidth) |
widthMax |
(float) vector of detected peak maximum peakwidth value or NULL (uf NULL no peakwidth) |
acquTime |
(POSIXct) vector of sample acquisition time as POSIXct or NULL (if NULL points are plotted in the order values are passed as input with the first on top or left) |
varName |
(str) Name of the variable to plot |
sampleColour |
(str) NULL or vector colour for each sample (same length
as |
rotateAxis |
(bool) if TRUE x and y axis are reversed |
verbose |
(bool) if TRUE message when NA scans are removed |
Grob (ggplot object)
## Input data apexVal <- c(1, 2, 3, 4) minVal <- c(0, 0, 2, 2) maxVal <- c(2, 4, 4, 5) acqTime <- as.POSIXct(c('2017-07-13 21:06:14', '2017-07-14 21:06:14', '2017-07-15 21:06:14', '2017-07-16 21:06:14')) ## Plot 4 sampels with colour peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=NULL, varName='Test variable 1', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE) ## Plot 4 samples with colour by acquisition time peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=acqTime, varName='Test variable 2', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE) ## Plot 4 samples with colour, rotate axis peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=NULL, varName='Test variable 3', sampleColour=c('blue','red','green','orange'), rotateAxis=TRUE, verbose=FALSE) ## Plot 4 samples with colour by acquisition time, rotate axis peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=acqTime, varName='Test variable 4', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE)## Input data apexVal <- c(1, 2, 3, 4) minVal <- c(0, 0, 2, 2) maxVal <- c(2, 4, 4, 5) acqTime <- as.POSIXct(c('2017-07-13 21:06:14', '2017-07-14 21:06:14', '2017-07-15 21:06:14', '2017-07-16 21:06:14')) ## Plot 4 sampels with colour peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=NULL, varName='Test variable 1', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE) ## Plot 4 samples with colour by acquisition time peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=acqTime, varName='Test variable 2', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE) ## Plot 4 samples with colour, rotate axis peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=NULL, varName='Test variable 3', sampleColour=c('blue','red','green','orange'), rotateAxis=TRUE, verbose=FALSE) ## Plot 4 samples with colour by acquisition time, rotate axis peakPantheR_plotPeakwidth(apexValue=apexVal, widthMin=minVal,widthMax=maxVal, acquTime=acqTime, varName='Test variable 4', sampleColour=c('blue','red','green','orange'), rotateAxis=FALSE, verbose=FALSE)
Simple plot of an Extracted Ion Chromatogram (EIC) from a raw data file and a provided mz and rt window. Return ggplot plot object.
peakPantheR_quickEIC( spectraPath, rt, mz, valuesOnly = "Plot", centroided = TRUE, msLevel = 1L, verbose = TRUE )peakPantheR_quickEIC( spectraPath, rt, mz, valuesOnly = "Plot", centroided = TRUE, msLevel = 1L, verbose = TRUE )
spectraPath |
(str) Path to the raw data file to read (uses 'MSnbase::readMSData()') |
rt |
(numeric(2) or two-column matrix) the lower and upper retention time range from which the data should be extracted. If a matrix is passed, each row corresponds to a different window. If not provided, the full retention time range will be extracted. |
mz |
(numeric(2) or two-column matrix) the lower and upper mass range from which the data should be extracted. If a matrix is passed, each row corresponds to a different window. If not provided, the full mass range will be extracted. |
valuesOnly |
(str) If 'Raw' only load the file and return a table of raw extracted values (exported version of 'extractSignalRawData()'). If 'EIC' return a table of EIC data point. Else return the EIC plot (default). |
centroided |
(bool) Indicate to 'MSnbase::readMSData()' whether the spectra file is centroided or not (default to 'TRUE'). |
msLevel |
(int) The MS level at which the data should be extracted (default to MS level 1). |
verbose |
(bool) Output proress information or not. |
Grob (ggplot object) of the EIC plot, if ‘valuesOnly=’Raw'' returns a data.frame of raw datapoints with as columns 'rt', 'mz' and 'int'. If ‘valuesOnly=’EIC'' returns a data.frame of EIC datapoints with as columns 'rt' and 'int'.
## Use a file form the faahKO package and plot an EIC of interest library(faahKO) spectraPath <- system.file('cdf/KO/ko15.CDF',package='faahKO') peakPantheR_quickEIC(spectraPath, rt = c(3290., 3410.), mz = c(522.194778, 522.205222))## Use a file form the faahKO package and plot an EIC of interest library(faahKO) spectraPath <- system.file('cdf/KO/ko15.CDF',package='faahKO') peakPantheR_quickEIC(spectraPath, rt = c(3290., 3410.), mz = c(522.194778, 522.205222))
Using reference samples (referenceSpectraFiles), save
(to saveFolder) each ROI EIC (ROI) and reports the mean apex RT
for all IS (IS_ROI) across samples
peakPantheR_ROIStatistics( referenceSpectraFiles, saveFolder, ROI = NULL, IS_ROI = NULL, sampleColour = NULL, ncores = 0, saveISPlots = TRUE, verbose = TRUE )peakPantheR_ROIStatistics( referenceSpectraFiles, saveFolder, ROI = NULL, IS_ROI = NULL, sampleColour = NULL, ncores = 0, saveISPlots = TRUE, verbose = TRUE )
referenceSpectraFiles |
(str) A character vector of paths to the reference spectra files |
saveFolder |
(str) Path to the folder where EICs and IS mean RT
( |
ROI |
(data.frame) NULL or a data.frame of Regions Of Interest (ROI)
with compounds as row and ROI parameters as columns: |
IS_ROI |
(data.frame) NULL or a data.frame of IS ROI with IS as row and
ROI parameters as columns: |
sampleColour |
(str) NULL or vector colour for each sample |
ncores |
(int) Number of cores to use to integrate IS in parallel |
saveISPlots |
(bool) If TRUE save a diagnostic plot for each IS to
|
verbose |
(bool) If TRUE message progress |
None
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 2 samples and 1 targeted ## compound # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 1, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(1)) # input refSpecFiles <- spectraPaths input_ROI <- targetFeatTable input_IS_ROI <- targetFeatTable sampleColour <- c('blue', 'red') # temporary saveFolder saveFolder1 <- tempdir() # Calculate ROI statiscs peakPantheR_ROIStatistics(refSpecFiles, saveFolder1, ROI=input_ROI, IS_ROI=input_IS_ROI, sampleColour=sampleColour, ncores=0, saveISPlots=TRUE, verbose=TRUE) }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 2 samples and 1 targeted ## compound # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 1, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(1)) # input refSpecFiles <- spectraPaths input_ROI <- targetFeatTable input_IS_ROI <- targetFeatTable sampleColour <- c('blue', 'red') # temporary saveFolder saveFolder1 <- tempdir() # Calculate ROI statiscs peakPantheR_ROIStatistics(refSpecFiles, saveFolder1, ROI=input_ROI, IS_ROI=input_IS_ROI, sampleColour=sampleColour, ncores=0, saveISPlots=TRUE, verbose=TRUE) }
Report for a raw spectra the TIC, acquisition time, integrated targeted features, fitted curves and datapoints for each region of interest. Optimised to reduce the number of file access. Features not detected can be integrated using fallback integration regions (FIR).
peakPantheR_singleFileSearch( singleSpectraDataPath, targetFeatTable, peakStatistic = FALSE, plotEICsPath = NA, getAcquTime = FALSE, FIR = NULL, centroided = TRUE, curveModel = "skewedGaussian", verbose = TRUE, ... )peakPantheR_singleFileSearch( singleSpectraDataPath, targetFeatTable, peakStatistic = FALSE, plotEICsPath = NA, getAcquTime = FALSE, FIR = NULL, centroided = TRUE, curveModel = "skewedGaussian", verbose = TRUE, ... )
singleSpectraDataPath |
(str) path to netCDF or mzML raw data file (centroided, only with the channel of interest). |
targetFeatTable |
a |
peakStatistic |
(bool) If TRUE calculates additional peak statistics: 'ppm_error', 'rt_dev_sec', 'tailing factor' and 'asymmetry factor' |
plotEICsPath |
(str or NA) If not NA, will save a .png of all ROI
EICs at the path provided ( |
getAcquTime |
(bool) If TRUE will extract sample acquisition date-time from the mzML metadata (the additional file access will impact run time) |
FIR |
(data.frame or NULL) If not NULL, integrate Fallback Integration
Regions (FIR) when a feature is not found. Compounds as row are identical to
|
centroided |
(bool) use TRUE if the data is centroided, used by
|
curveModel |
(str) specify the peak-shape model to fit,
by default |
verbose |
(bool) If TRUE message calculation progress, time taken and number of features found |
... |
Passes arguments to |
a list: list()$TIC (int) TIC value,
list()$peakTable (data.frame) targeted features results
(see Details), list()$curveFit (list) list of
peakPantheR_curveFit or NA for each ROI, list()$acquTime
(POSIXct or NA) date-time of sample acquisition from mzML metadata,
list()$ROIsDataPoint (list) a list of data.frame of raw
data points for each ROI (retention time 'rt', mass 'mz' and intensity 'int'
(as column) of each raw data points (as row)).
The returned peakTable data.frame is structured as follow:
| cpdID | database compound ID |
| cpdName | compound name |
| found | was the peak found |
| rt | retention time of peak apex (sec) |
| rtMin | leading edge of peak retention time (sec) determined at 0.5% of apex intensity |
| rtMax | trailing edge of peak retention time (sec) determined at 0.5% of apex intensity |
| mz | weighted (by intensity) mean of peak m/z across scans |
| mzMin | m/z peak minimum (between rtMin, rtMax) |
| mzMax | m/z peak maximum (between rtMin, rtMax) |
| peakArea | integrated peak area |
| peakAreaRaw | integrated peak area from raw data points |
| maxIntMeasured | maximum peak intensity in raw data |
| maxIntPredicted | maximum peak intensity based on curve fit |
| is_filled | Logical indicate if the feature was integrated using FIR (Fallback Integration Region) |
| ppm_error | difference in ppm between the expected and measured m/z |
| rt_dev_sec | difference in seconds between the expected and measured rt |
| tailingFactor | the tailing factor is a measure of peak tailing.It is defined as the distance from the front slope of the peak to the back slope divided by twice the distance from the center line of the peak to the front slope, with all measurements made at 5% of the maximum peak height. The tailing factor of a peak will typically be similar to the asymmetry factor for the same peak, but the two values cannot be directly converted |
| asymmetryFactor | the asymmetry factor is a measure of peak tailing. It is defined as the distance from the center line of the peak to the back slope divided by the distance from the center line of the peak to the front slope, with all measurements made at 10% of the maximum peak height. The asymmetry factor of a peak will typically be similar to the tailing factor for the same peak, but the two values cannot be directly converted |
Other peakPantheR:
peakPantheRAnnotation,
peakPantheR_parallelAnnotation()
Other parallelAnnotation:
peakPantheRAnnotation,
peakPantheR_parallelAnnotation()
if(requireNamespace('faahKO')){ ## Load data library(faahKO) netcdfFilePath <- system.file('cdf/KO/ko15.CDF', package = 'faahKO') ## targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) res <- peakPantheR_singleFileSearch(netcdfFilePath,targetFeatTable, peakStatistic=TRUE) # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 2 windows # Data read in: 0.16 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Found 2/2 features in 0.05 secs # Peak statistics done in: 0 secs # Feature search done in: 0.75 secs res # $TIC # [1] 2410533091 # # $peakTable # found rtMin rt rtMax mzMin mz mzMax peakArea # 1 TRUE 3309.759 3346.828 3385.410 522.1948 522.2 522.2052 26133727 # 2 TRUE 3345.377 3386.529 3428.279 496.2000 496.2 496.2000 35472141 # peakAreaRaw maxIntMeasured maxIntPredicted cpdID cpdName is_filled # 1 26071378 889280 901015.8 ID-1 Cpd 1 FALSE # 2 36498367 1128960 1113576.7 ID-2 Cpd 2 FALSE # ppm_error rt_dev_sec tailingFactor asymmetryFactor # 1 0.02337616 1.9397590 1.015357 1.026824 # 2 0.02460103 0.9518072 1.005378 1.009318 # # $acquTime # [1] NA # # # $curveFit # $curveFit[[1]] # $amplitude # [1] 162404.8 # # $center # [1] 3341.888 # # $sigma # [1] 0.07878613 # # $gamma # [1] 0.00183361 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit' # # $curveFit[[2]] # $amplitude # [1] 199249.1 # # $center # [1] 3382.577 # # $sigma # [1] 0.07490442 # # $gamma # [1] 0.00114719 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit' # # # $ROIsDataPoint # $ROIsDataPoint[[1]] # rt mz int # 1 3315.154 522.2 2187 # 2 3316.719 522.2 3534 # 3 3318.284 522.2 6338 # 4 3319.849 522.2 11718 # 5 3321.414 522.2 21744 # 6 3322.979 522.2 37872 # 7 3324.544 522.2 62424 # 8 3326.109 522.2 98408 # 9 3327.673 522.2 152896 # 10 3329.238 522.2 225984 # ... # # $ROIsDataPoint[[2]] # rt mz int # 1 3280.725 496.2 1349 # 2 3290.115 496.2 2069 # 3 3291.680 496.2 3103 # 4 3293.245 496.2 5570 # 5 3294.809 496.2 10730 # 6 3296.374 496.2 20904 # 7 3297.939 496.2 38712 # 8 3299.504 496.2 64368 # 9 3301.069 496.2 97096 # 10 3302.634 496.2 136320 # ... }if(requireNamespace('faahKO')){ ## Load data library(faahKO) netcdfFilePath <- system.file('cdf/KO/ko15.CDF', package = 'faahKO') ## targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) res <- peakPantheR_singleFileSearch(netcdfFilePath,targetFeatTable, peakStatistic=TRUE) # Polarity can not be extracted from netCDF files, please set manually the # polarity with the 'polarity' method. # Reading data from 2 windows # Data read in: 0.16 secs # Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for # mzMin/mzMax calculation, approximate mz and returning ROI$mzMin and # ROI$mzMax for ROI #1 # Found 2/2 features in 0.05 secs # Peak statistics done in: 0 secs # Feature search done in: 0.75 secs res # $TIC # [1] 2410533091 # # $peakTable # found rtMin rt rtMax mzMin mz mzMax peakArea # 1 TRUE 3309.759 3346.828 3385.410 522.1948 522.2 522.2052 26133727 # 2 TRUE 3345.377 3386.529 3428.279 496.2000 496.2 496.2000 35472141 # peakAreaRaw maxIntMeasured maxIntPredicted cpdID cpdName is_filled # 1 26071378 889280 901015.8 ID-1 Cpd 1 FALSE # 2 36498367 1128960 1113576.7 ID-2 Cpd 2 FALSE # ppm_error rt_dev_sec tailingFactor asymmetryFactor # 1 0.02337616 1.9397590 1.015357 1.026824 # 2 0.02460103 0.9518072 1.005378 1.009318 # # $acquTime # [1] NA # # # $curveFit # $curveFit[[1]] # $amplitude # [1] 162404.8 # # $center # [1] 3341.888 # # $sigma # [1] 0.07878613 # # $gamma # [1] 0.00183361 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit' # # $curveFit[[2]] # $amplitude # [1] 199249.1 # # $center # [1] 3382.577 # # $sigma # [1] 0.07490442 # # $gamma # [1] 0.00114719 # # $fitStatus # [1] 2 # # $curveModel # [1] 'skewedGaussian' # # attr(,'class') # [1] 'peakPantheR_curveFit' # # # $ROIsDataPoint # $ROIsDataPoint[[1]] # rt mz int # 1 3315.154 522.2 2187 # 2 3316.719 522.2 3534 # 3 3318.284 522.2 6338 # 4 3319.849 522.2 11718 # 5 3321.414 522.2 21744 # 6 3322.979 522.2 37872 # 7 3324.544 522.2 62424 # 8 3326.109 522.2 98408 # 9 3327.673 522.2 152896 # 10 3329.238 522.2 225984 # ... # # $ROIsDataPoint[[2]] # rt mz int # 1 3280.725 496.2 1349 # 2 3290.115 496.2 2069 # 3 3291.680 496.2 3103 # 4 3293.245 496.2 5570 # 5 3294.809 496.2 10730 # 6 3296.374 496.2 20904 # 7 3297.939 496.2 38712 # 8 3299.504 496.2 64368 # 9 3301.069 496.2 97096 # 10 3302.634 496.2 136320 # ... }
peakPantheR Graphical User Interface (GUI) implements all the functions for
the parallel detection, integration and reporting of pre-defined features in
multiple mass spectrometry data files. To exit press ESC in the
command line.
peakPantheR_start_GUI(browser = TRUE)peakPantheR_start_GUI(browser = TRUE)
browser |
If TRUE open the graphical user interface in a web browser instead of a R window. Default is TRUE |
None, start GUI. To exit press ESC in the command line.
print("Start graphical interface, press 'ESC' in the command line to stop") # peakPantheR_start_GUI()print("Start graphical interface, press 'ESC' in the command line to stop") # peakPantheR_start_GUI()
The peakPantheRAnnotation class is designed to run and
store peakPantheR parallel annotation results. Instances of the class are
created with the peakPantheRAnnotation constructor function, which
initialises an object of proper dimension with spectraPaths (set
samples to process) and targetFeatTable (set compounds to target).
spectraPaths is a character vector of spectra file paths.
targetFeatTable is a data.frame of compounds to target
as rows and parameters as columns: cpdID (int), cpdName (str),
rtMin (float in seconds), rt (float in seconds, or NA),
rtMax (float in seconds), mzMin (float), mz (float or
NA), mzMax (float).
peakPantheRAnnotation(): create an instance of the
peakPantherAnnotation class.
peakPantheRAnnotation(spectraPaths = NULL, targetFeatTable = NULL, cpdID = character(), cpdName = character(), ROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), FIR = data.frame(rtMin = numeric(), rtMax = numeric(), mzMin = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), uROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), filepath = character(), cpdMetadata = data.frame(), spectraMetadata = data.frame(), acquisitionTime = character(), uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, TIC = numeric(), peakTables = list(), dataPoints = list(), peakFit = list(), isAnnotated = FALSE) peakPantheRAnnotation(spectraPaths = NULL, targetFeatTable = NULL, cpdID = character(), cpdName = character(), ROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), FIR = data.frame(rtMin = numeric(), rtMax = numeric(), mzMin = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), uROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), filepath = character(), cpdMetadata = data.frame(), spectraMetadata = data.frame(), acquisitionTime = character(), uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, TIC = numeric(), peakTables = list(), dataPoints = list(), peakFit = list(), isAnnotated = FALSE)peakPantheRAnnotation(spectraPaths = NULL, targetFeatTable = NULL, cpdID = character(), cpdName = character(), ROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), FIR = data.frame(rtMin = numeric(), rtMax = numeric(), mzMin = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), uROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), filepath = character(), cpdMetadata = data.frame(), spectraMetadata = data.frame(), acquisitionTime = character(), uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, TIC = numeric(), peakTables = list(), dataPoints = list(), peakFit = list(), isAnnotated = FALSE) peakPantheRAnnotation(spectraPaths = NULL, targetFeatTable = NULL, cpdID = character(), cpdName = character(), ROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), FIR = data.frame(rtMin = numeric(), rtMax = numeric(), mzMin = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), uROI = data.frame(rtMin = numeric(), rt = numeric(), rtMax = numeric(), mzMin = numeric(), mz = numeric(), mzMax = numeric(), stringsAsFactors = FALSE), filepath = character(), cpdMetadata = data.frame(), spectraMetadata = data.frame(), acquisitionTime = character(), uROIExist = FALSE, useUROI = FALSE, useFIR = FALSE, TIC = numeric(), peakTables = list(), dataPoints = list(), peakFit = list(), isAnnotated = FALSE)
spectraPaths |
NULL or a character vector of spectra file paths, to set samples to process |
targetFeatTable |
NULL or a |
cpdID |
A character vector of compound IDs, of length number of compounds |
cpdName |
A character vector of compound names, of length number of compounds |
ROI |
A data.frame of Regions Of Interest (ROI) with compounds as
row and ROI parameters as columns: |
FIR |
A data.frame of Fallback Integration Regions (FIR) with
compounds as row and FIR parameters as columns: |
uROI |
A data.frame of updated Regions Of Interest (uROI) with
compounds as row and uROI parameters as columns: |
filepath |
A character vector of file paths, of length number of spectra files |
cpdMetadata |
A data.frame of compound metadata, with compounds as row and metadata as columns |
spectraMetadata |
A data.frame of sample metadata, with samples as row and metadata as columns |
acquisitionTime |
A character vector of acquisition date-time (converted from POSIXct) or NA |
uROIExist |
A logical stating if uROI have been set |
useUROI |
A logical stating if uROI are to be used |
useFIR |
A logical stating if FIR are to be used |
TIC |
A numeric vector of TIC or NA, of length number of spectra files |
peakTables |
A list of peakTable data.frame, of length number of spectra files. Each peakTable data.frame has compounds as rows and peak annotation results as columns. |
dataPoints |
A list of length number of spectra files. Each list element
is a ROIsDataPoint list of |
peakFit |
A list of length number of spectra files. Each list element is
a curveFit list of |
isAnnotated |
A logical stating in the annotation took place |
The validObject method ensures the conformity of an object to
the peakPantheRAnnotation-class. The number of compounds is based on
cpdID() length, and the number of samples is based on
filepath() length. Slot type is not checked as setClass
enforces it. peakTables and EICs type are checked on the first list element.
annotationTable(object, column) where column is a column from
peakTable, returns a data.frame of values with the samples as rows,
ROI as columns.
(peakPantheRAnnotation)
cpdIDA character vector of compound IDs, of length number of compounds
cpdNameA character vector of compound names, of length number of compounds
ROIA data.frame of Regions Of Interest (ROI) with
compounds as row and
ROI parameters as columns: rtMin (float in seconds), rt (float
in seconds, or NA), rtMax (float in seconds), mzMin
(float), mz (float or NA), mzMax (float).
FIRA data.frame of Fallback Integration Regions (FIR)
with compounds
as row and FIR parameters as columns: rtMin (float in seconds),
rtMax (float in seconds), mzMin (float), mzMax (float).
uROIA data.frame of updated Regions Of Interest
(uROI) with compounds
as row and uROI parameters as columns: rtMin (float in seconds),
rt (float in seconds, or NA), rtMax (float in seconds),
mzMin (float), mz (float or NA), mzMax (float).
filepathA character vector of file paths, of length number of spectra files
cpdMetadataA data.frame of compound metadata, with compounds as row and metadata as columns
spectraMetadataA data.frame of sample metadata, with samples as row and metadata as columns
acquisitionTimeA character vector of acquisition date-time (converted from POSIXct) or NA
uROIExistA logical stating if uROI have been set
useUROIA logical stating if uROI are to be used
useFIRA logical stating if FIR are to be used
TICA numeric vector of TIC or NA, of length number of spectra files
peakTablesA list of peakTable data.frame, of length number of spectra files. Each peakTable data.frame has compounds as rows and peak annotation results as columns.
dataPointsA list of length number of spectra files.
Each list element is a ROIsDataPoint list of data.frame
of raw data points for each ROI/uROI (retention time 'rt',
mass 'mz' and intensity 'int' (as column) of each raw data points (as row))
peakFitA list of length number of spectra files.
Each list element is a curveFit list of
peakPantheR_curveFit or NA for each ROI
isAnnotatedA logical stating if the annotation has taken place
The peakTables data.frame are structured as follow:
| cpdID | database compound ID |
| cpdName | compound name |
| found | was the peak found |
| rt | retention time of peak apex (sec) |
| rtMin | leading edge of peak retention time (sec) determined at 0.5% of apex intensity |
| rtMax | trailing edge of peak retention time (sec) determined at 0.5% of apex intensity |
| mz | weighted (by intensity) mean of peak m/z across scans |
| mzMin | m/z peak minimum (between rtMin, rtMax) |
| mzMax | m/z peak maximum (between rtMin, rtMax) |
| peakArea | integrated peak area |
| peakAreaRaw | integrated peak area from raw data points |
| maxIntMeasured | maximum peak intensity in raw data |
| maxIntPredicted | maximum peak intensity based on curve fit |
| is_filled | Logical indicate if the feature was integrated using FIR (Fallback Integration Region) |
| ppm_error | difference in ppm between the expected and measured m/z |
| rt_dev_sec | difference in seconds between the expected and measured rt |
| tailingFactor | the tailing factor is a measure of peak tailing.It is defined as the distance from the front slope of the peak to the back slope divided by twice the distance from the center line of the peak to the front slope, with all measurements made at 5% of the maximum peak height. The tailing factor of a peak will typically be similar to the asymmetry factor for the same peak, but the two values cannot be directly converted |
| asymmetryFactor | the asymmetry factor is a measure of peak tailing. It is defined as the distance from the center line of the peak to the back slope divided by the distance from the center line of the peak to the front slope, with all measurements made at 10% of the maximum peak height. The asymmetry factor of a peak will typically be similar to the tailing factor for the same peak, but the two values cannot be directly converted |
Other peakPantheR:
peakPantheR_parallelAnnotation(),
peakPantheR_singleFileSearch()
Other parallelAnnotation:
peakPantheR_parallelAnnotation(),
peakPantheR_singleFileSearch()
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(),2,8,dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotation # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated slotNames(annotation) # [1] 'cpdID' 'cpdName' 'ROI' 'FIR' 'uROI' # [6] 'filepath' 'cpdMetadata' 'spectraMetadata' 'acquisitionTime' # [10] 'uROIExist' 'useUROI' 'useFIR' 'TIC' 'peakTables' # [15] 'dataPoints' 'peakFit' 'isAnnotated' ## Slots shouldn't be accessed directly, accessors are available: cpdID(annotation) # [1] 'ID-1' 'ID-2' cpdName(annotation) # [1] 'Cpd 1' 'Cpd 2' ROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 3310 3344.888 3390 522.1948 522.2 522.2052 ID-1 Cpd 1 # 2 3280 3385.577 3440 496.1950 496.2 496.2050 ID-2 Cpd 2 FIR(annotation) # rtMin rtMax mzMin mzMax cpdID cpdName # 1 NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA ID-2 Cpd 2 uROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 NA NA NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA NA NA ID-2 Cpd 2 filepath(annotation) # [1] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF' # [2]'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF' # [3] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF' cpdMetadata(annotation) # data frame with 0 columns and 2 rows spectraMetadata(annotation) # data frame with 0 columns and 3 rows acquisitionTime(annotation) # [1] NA NA NA uROIExist(annotation) # [1] FALSE useUROI(annotation) # [1] FALSE useFIR(annotation) # [1] FALSE TIC(annotation) # [1] NA NA NA peakTables(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL dataPoints(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL peakFit(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL isAnnotated(annotation) # [1] FALSE }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(),2,8,dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) annotation # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated slotNames(annotation) # [1] 'cpdID' 'cpdName' 'ROI' 'FIR' 'uROI' # [6] 'filepath' 'cpdMetadata' 'spectraMetadata' 'acquisitionTime' # [10] 'uROIExist' 'useUROI' 'useFIR' 'TIC' 'peakTables' # [15] 'dataPoints' 'peakFit' 'isAnnotated' ## Slots shouldn't be accessed directly, accessors are available: cpdID(annotation) # [1] 'ID-1' 'ID-2' cpdName(annotation) # [1] 'Cpd 1' 'Cpd 2' ROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 3310 3344.888 3390 522.1948 522.2 522.2052 ID-1 Cpd 1 # 2 3280 3385.577 3440 496.1950 496.2 496.2050 ID-2 Cpd 2 FIR(annotation) # rtMin rtMax mzMin mzMax cpdID cpdName # 1 NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA ID-2 Cpd 2 uROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 NA NA NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA NA NA ID-2 Cpd 2 filepath(annotation) # [1] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko15.CDF' # [2]'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko16.CDF' # [3] 'C:/R/R-3.6.0/library/faahKO/cdf/KO/ko18.CDF' cpdMetadata(annotation) # data frame with 0 columns and 2 rows spectraMetadata(annotation) # data frame with 0 columns and 3 rows acquisitionTime(annotation) # [1] NA NA NA uROIExist(annotation) # [1] FALSE useUROI(annotation) # [1] FALSE useFIR(annotation) # [1] FALSE TIC(annotation) # [1] NA NA NA peakTables(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL dataPoints(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL peakFit(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL isAnnotated(annotation) # [1] FALSE }
peakTables accessor with cpdID and cpdName added back
## S4 method for signature 'peakPantheRAnnotation' peakTables(object)## S4 method for signature 'peakPantheRAnnotation' peakTables(object)
object |
peakPantheRAnnotation |
(data.frame) A list of peakTable data.frame, of length number of spectra files. Each peakTable data.frame has compounds as rows and peak annotation results as columns, with added compound ID and name.
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation peakTables(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation peakTables(annotation) # [[1]] # NULL # [[2]] # NULL # [[3]] # NULL }
Plot a ROI across multiple samples (x axis is RT, y axis is intensity) with the matching detected peak rt and peakwidth under it. If curveFit is provided, the fitted curve for each compound is added. RT and peakwidth are plotted in the order spectra are passed, with the first spectra on top.
plotEICDetectedPeakwidth( ROIDataPointSampleList, cpdID, cpdName, rt, rtMin, rtMax, mzMin, mzMax, ratio = 0.85, sampling = 250, curveFitSampleList = NULL, sampleColour = NULL, verbose = TRUE )plotEICDetectedPeakwidth( ROIDataPointSampleList, cpdID, cpdName, rt, rtMin, rtMax, mzMin, mzMax, ratio = 0.85, sampling = 250, curveFitSampleList = NULL, sampleColour = NULL, verbose = TRUE )
ROIDataPointSampleList |
(list) list of |
cpdID |
(str) Compound ID |
cpdName |
(str) Compound Name |
rt |
(float) vector of detected peak apex retention time (in sec) |
rtMin |
(float) vector of detected peak minimum retention time (in sec) |
rtMax |
(float) vector ofdetected peak maximum retention time (in sec) |
mzMin |
(float) ROI minimum m/z (matching EIC) |
mzMax |
(float) ROI maximum m/z (matching EIC) |
ratio |
(float) value between 0 and 1 defining the vertical percentage taken by the EICs subplot |
sampling |
(int) Number of points to employ when plotting fittedCurve |
curveFitSampleList |
(list) NULL or a list of
|
sampleColour |
(str) NULL or vector colour for each sample (same length
as |
verbose |
(bool) if TRUE message when NA scans are removed |
Grob (ggplot object)
Plot the histogram and density of the variable
plotHistogram(var, varName = "Variable", density = TRUE, ...)plotHistogram(var, varName = "Variable", density = TRUE, ...)
var |
(float) vector of values to plot |
varName |
(str) Name of the variable to plot |
density |
(bool) If TRUE plot overlay the density on the variable |
... |
Passes arguments to ggplot2::geom_histogram, e.g. |
Grob (ggplot object)
Evaluate fitted curve values at x data points
predictCurve(fittedCurve, x)predictCurve(fittedCurve, x)
fittedCurve |
(peakPantheR_curveFit) A 'peakPantheR_curveFit': a list of
curve fitting parameters, curve shape model |
x |
(numeric) values at which to evaluate the fitted curve |
## Examples cannot be computed as the function is not exported: ## Input a fitted curve fittedCurve <- list(amplitude=275371.1, center=3382.577, sigma=0.07904697, gamma=0.001147647, fitStatus=2, curveModel='skewedGaussian') class(fittedCurve) <- 'peakPantheR_curveFit' input_x <- c(3290, 3300, 3310, 3320, 3330, 3340, 3350, 3360, 3370, 3380, 3390, 3400, 3410)
## Predict y at each input_x pred_y <- predictCurve(fittedCurve, input_x) pred_y # [1] 2.347729e-08 1.282668e-05 3.475590e-03 4.676579e-01 3.129420e+01 # [6] 1.043341e+03 1.736915e+04 1.447754e+05 6.061808e+05 1.280037e+06 # [11] 1.369651e+06 7.467333e+05 2.087477e+05
fitted curve values at x
Process target region parameters with uROI and FIR (cpdID, cpdName,
ROI_rt, ROI_mz, ROI_rtMin, ROI_rtMax, ROI_mzMin, ROI_mzMax, uROI_rtMin,
uROI_rtMax, uROI_mzMin, uROI_mzMax, uROI_rt, uROI_mz, FIR_rtMin, FIR_rtMax,
FIR_mzMin, FIR_mzMax) and return input variables for
peakPantheRAnnotation()
prepare_advanced_target_parameters(paramTable, verbose)prepare_advanced_target_parameters(paramTable, verbose)
paramTable |
(data.frame) Target region parameters |
verbose |
(bool) If TRUE message progress |
(list) List of targetFeatTable, uROI, FIR, uROIExist
Process the simple target region parameters (cpdID, cpdName, mzMin,
mzMax, mz, rtMin, rtMax, rt) and return input variables for
peakPantheRAnnotation()
prepare_basic_target_parameters(paramTable)prepare_basic_target_parameters(paramTable)
paramTable |
(data.frame) Target region parameters |
(list) List of targetFeatTable, uROI, FIR, uROIExist
Reset a peakPantheRAnnotation (remove results and set
isAnnotated=FALSE). If a different number of samples (
spectraPaths) or compounds (targetFeatTable) are passed, the
object will be initialised to the new size. For input values left as NULL,
the slots (filepath (from spectraPaths), ROI,
cpdID, cpdName (from targetFeatTable), uROI,
FIR, cpdMetadata, spectraMetadata, uROIExist,
useUROI and useFIR) will be filled with values from
previousAnnotation.
## S4 method for signature 'peakPantheRAnnotation' resetAnnotation( previousAnnotation, spectraPaths = NULL, targetFeatTable = NULL, uROI = NULL, FIR = NULL, cpdMetadata = NULL, spectraMetadata = NULL, uROIExist = NULL, useUROI = NULL, useFIR = NULL, verbose = TRUE, ... )## S4 method for signature 'peakPantheRAnnotation' resetAnnotation( previousAnnotation, spectraPaths = NULL, targetFeatTable = NULL, uROI = NULL, FIR = NULL, cpdMetadata = NULL, spectraMetadata = NULL, uROIExist = NULL, useUROI = NULL, useFIR = NULL, verbose = TRUE, ... )
previousAnnotation |
(peakPantheRAnnotation) object to reset |
spectraPaths |
NULL or a character vector of spectra file paths, to set samples to process |
targetFeatTable |
NULL or a |
uROI |
NULL or a data.frame of updated Regions Of Interest (uROI) with
compounds as row and uROI parameters as columns: |
FIR |
NULL or a data.frame of Fallback Integration Regions (FIR) with
compounds as row and FIR parameters as columns: |
cpdMetadata |
NULL or a data.frame of compound metadata, with compounds as row and metadata as columns |
spectraMetadata |
NULL or a data.frame of sample metadata, with samples as row and metadata as columns |
uROIExist |
NULL or a logical stating if uROI have been set |
useUROI |
NULL or a logical stating if uROI are to be used |
useFIR |
NULL or a logical stating if FIR are to be used |
verbose |
(bool) If TRUE message progress |
... |
Additional slots and values to set when resetting the object
( |
(peakPantheRAnnotation) object reset with previous results removed and slots updated
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) smallAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 2 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated # Reset and change number of spectra newSpectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) largerAnnotation <- resetAnnotation(smallAnnotation, spectraPaths=newSpectraPaths, verbose=TRUE) largerAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) smallAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 2 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated # Reset and change number of spectra newSpectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) largerAnnotation <- resetAnnotation(smallAnnotation, spectraPaths=newSpectraPaths, verbose=TRUE) largerAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 3 samples. # updated ROI do not exist (uROI) # does not use updated ROI (uROI) # does not use fallback integration regions (FIR) # is not annotated }
uROIExist=FALSE)Reset FIR windows to uROI or ROI values
Reset FIR windows to uROI (or ROI if uROIExist=FALSE)
## S4 method for signature 'peakPantheRAnnotation' resetFIR(object, verbose)## S4 method for signature 'peakPantheRAnnotation' resetFIR(object, verbose)
object |
(peakPantheRAnnotation) object for which FIR are to be reset |
verbose |
(bool) If TRUE message progress |
(peakPantheRAnnotation) object with FIR values reset
## Initialise a peakPantheRAnnotation object with 2 targeted compounds ## targetFeatTable input_targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) input_targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3., 1., 4., 5., 2., 6.) input_targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 19., 17., 20., 21., 18., 22.) input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric) ## FIR input_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c('rtMin', 'rtMax', 'mzMin', 'mzMax'))), stringsAsFactors=FALSE) input_FIR[1,] <- c(13., 14., 15., 16.) input_FIR[2,] <- c(29., 30., 31., 32.) annotation <- peakPantheRAnnotation(targetFeatTable = input_targetFeatTable, FIR = input_FIR, uROIExist = FALSE) ## Reset FIR with ROI values as uROI are not set updatedAnnotation <- resetFIR(annotation, verbose=TRUE) # FIR will be reset with ROI values as uROI values are not set## Initialise a peakPantheRAnnotation object with 2 targeted compounds ## targetFeatTable input_targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) input_targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3., 1., 4., 5., 2., 6.) input_targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 19., 17., 20., 21., 18., 22.) input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric) ## FIR input_FIR <- data.frame(matrix(vector(), 2, 4, dimnames=list(c(), c('rtMin', 'rtMax', 'mzMin', 'mzMax'))), stringsAsFactors=FALSE) input_FIR[1,] <- c(13., 14., 15., 16.) input_FIR[2,] <- c(29., 30., 31., 32.) annotation <- peakPantheRAnnotation(targetFeatTable = input_targetFeatTable, FIR = input_FIR, uROIExist = FALSE) ## Reset FIR with ROI values as uROI are not set updatedAnnotation <- resetFIR(annotation, verbose=TRUE) # FIR will be reset with ROI values as uROI values are not set
Performs retention time correction to re-adjust the expected
retention time position of compounds.
Requires an annotated peakPantheRAnnotation object (isAnnotated=TRUE).
The original rt value is used as expected and the observed deviation
measured in the rt_dev_sec field is taken as the deviation to be
corrected.
## S4 method for signature 'peakPantheRAnnotation' retentionTimeCorrection( annotationObject, rtCorrectionReferences = NULL, method = "polynomial", params = list(polynomialOrder = 2), robust = FALSE, rtWindowWidth = 15, diagnostic = TRUE )## S4 method for signature 'peakPantheRAnnotation' retentionTimeCorrection( annotationObject, rtCorrectionReferences = NULL, method = "polynomial", params = list(polynomialOrder = 2), robust = FALSE, rtWindowWidth = 15, diagnostic = TRUE )
annotationObject |
(peakPantheRAnnotation) object with previous fit results to adjust retention time values in uROI and FIR annotationObject, rtCorrectionReferences=NULL, |
rtCorrectionReferences |
(list) of compounds IDs ( |
method |
(str) name of RT correction method to use (currently
|
params |
(list) list of parameters to pass to each correction method.
Currently allowed inputs are |
robust |
(bool) whether to use the RANSAC algorithm to flag and ignore outliers during retention time correction |
rtWindowWidth |
(numeric) full width in seconds of the retention time window defined around the corrected retention time value for each compound |
diagnostic |
(bool) If TRUE returns diagnostic plots (specific to each correction method) |
(list) containing entries 'annotation', with the new and retention
time corrected peakPantheRAnnotation, and 'plot' (if diagnostic=TRUE).
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # annotate files serially annotation_result <- peakPantheR_parallelAnnotation(smallAnnotation, ncores=0, verbose=TRUE) data_annotation <- annotation_result$annotation # Example with constant correction rtCorrectionOutput <- retentionTimeCorrection( annotationObject = data_annotation, rtCorrectionReferences=c('ID-1'), method='constant', params=list(), robust=FALSE, rtWindowWidth=15, diagnostic=TRUE) rtCorrectedAnnotation <- rtCorrectionOutput$annotation # rtCorrectedAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 2 samples. # updated ROI exists, with a modified rt (uROI) # uses updated ROI (uROI) # uses fallback integration regions (FIR) # is annotated rtCorrectionPlot <- rtCorrectionOutput$plot # rtCorrectedPlot # A ggplot2 object # Scatterplot where x=`r` in the and y=`rt_dev_sec` from data_annotation # Points colored depending on whether the reference was used to fit # the correction model }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) smallAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # annotate files serially annotation_result <- peakPantheR_parallelAnnotation(smallAnnotation, ncores=0, verbose=TRUE) data_annotation <- annotation_result$annotation # Example with constant correction rtCorrectionOutput <- retentionTimeCorrection( annotationObject = data_annotation, rtCorrectionReferences=c('ID-1'), method='constant', params=list(), robust=FALSE, rtWindowWidth=15, diagnostic=TRUE) rtCorrectedAnnotation <- rtCorrectionOutput$annotation # rtCorrectedAnnotation # An object of class peakPantheRAnnotation # 2 compounds in 2 samples. # updated ROI exists, with a modified rt (uROI) # uses updated ROI (uROI) # uses fallback integration regions (FIR) # is annotated rtCorrectionPlot <- rtCorrectionOutput$plot # rtCorrectedPlot # A ggplot2 object # Scatterplot where x=`r` in the and y=`rt_dev_sec` from data_annotation # Points colored depending on whether the reference was used to fit # the correction model }
ROI accessor returns targetFeatTable with cpdID, cpdName added
## S4 method for signature 'peakPantheRAnnotation' ROI(object)## S4 method for signature 'peakPantheRAnnotation' ROI(object)
object |
peakPantheRAnnotation |
(data.frame) target feature table with compounds as row and ROI parameters as columns
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 3310 3344.888 3390 522.1948 522.2 522.2052 ID-1 Cpd 1 # 2 3280 3385.577 3440 496.1950 496.2 496.2050 ID-2 Cpd 2 }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 3310 3344.888 3390 522.1948 522.2 522.2052 ID-1 Cpd 1 # 2 3280 3385.577 3440 496.1950 496.2 496.2050 ID-2 Cpd 2 }
Plot and save a .png of all ROI (x is RT, y is
intensity), with the matching detected peak rt and peakwidth under it.
saveSingleFileMultiEIC( ROIsDataPoint, curveFit, foundPeakTable, savePath, width = 15, height = 15, verbose = TRUE )saveSingleFileMultiEIC( ROIsDataPoint, curveFit, foundPeakTable, savePath, width = 15, height = 15, verbose = TRUE )
ROIsDataPoint |
(list) a list of |
curveFit |
(list) a list of |
foundPeakTable |
(data.frame) |
savePath |
(str) Full path to save a .png of all ROI EICs,
expect |
width |
(float) Width in cm for a single ROI plot (if more than one plot in total, 2 columns will be used). dpi set to a 100. |
height |
(float) height in a cm for a single ROI plot. dpi set to 100 |
verbose |
(bool) if TRUE message progress |
None
Guess function for initial skewed gaussian parameters and bounds, at the moment only checks the x position
skewedGaussian_guess(x, y)skewedGaussian_guess(x, y)
x |
(numeric) x values (e.g. retention time) |
y |
(numeric) y observed values (e.g. spectra intensity) |
A list of guessed starting parameters list()$init_params,
lower list()$lower_bounds and upper bounds list()$upper_bounds
($gamma, $center, $sigma, $amplitude)
Implementation of the Skewed Gaussian peak shape for use with minpack.lm
skewedGaussian_minpack.lm(params, xx)skewedGaussian_minpack.lm(params, xx)
params |
(list) skewed gaussian parameters ( |
xx |
(numeric) values at which to evalute the skewed gaussian |
value of the skewed gaussian evaluated at xx
Skewed Gaussian minpack.lm objective function, calculates residuals using the skewed gaussian Peak Shape
skewedGaussian_minpack.lm_objectiveFun(params, observed, xx)skewedGaussian_minpack.lm_objectiveFun(params, observed, xx)
params |
(list) skewed gaussian parameters ( |
observed |
(numeric) observed y value at xx |
xx |
(numeric) value at which to evalute the skewed gaussian |
difference between observed and expected skewed gaussian value evaluated at xx
Return a vector of spectra colours based on a metadata column
spectra_metadata_colourScheme_UI_helper( annot, splColrColumn = NULL )spectra_metadata_colourScheme_UI_helper( annot, splColrColumn = NULL )
annot |
(peakPantheRAnnotation) Annotation object |
splColrColumn |
(str) NULL, None or a spectraMetadata column for colouring each sample |
(character) Vector of colours
## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # colour scheme with no spectraMetadata outputAnnotationFeatureMetadata_UI_helper(emptyAnnotation) # NULL## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files spectraPaths <- c('./path/file1', './path/file2', './path/file3') # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) emptyAnnotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) # colour scheme with no spectraMetadata outputAnnotationFeatureMetadata_UI_helper(emptyAnnotation) # NULL
spectraMetadata accessor
## S4 method for signature 'peakPantheRAnnotation' spectraMetadata(object)## S4 method for signature 'peakPantheRAnnotation' spectraMetadata(object)
object |
peakPantheRAnnotation |
(data.frame) A data.frame of sample metadata, with samples as row and metadata as columns
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values not initialised spectraMetadata(annotation) # data frame with 0 columns and 3 rows }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values not initialised spectraMetadata(annotation) # data frame with 0 columns and 3 rows }
Return spectraPaths and spectraMetadata from a .csv file (if available). If reading from the spectraMetadata file, the spectraPaths are taken from the 'filepath' column
spectraPaths_and_metadata_UI_helper( spectraPaths = NULL, spectraMetadataPath = NULL )spectraPaths_and_metadata_UI_helper( spectraPaths = NULL, spectraMetadataPath = NULL )
spectraPaths |
NULL or character vector of spectra file paths, to set samples to process |
spectraMetadataPath |
NULL or path to a csv of spectra metadata, with spectra as row and metadata as columns. (spectraPaths in column 'filepath') |
spectraPaths (str) and spectraMetadata (DataFrame or NULL) read from the CSV file
## Input data # spectraPath input_spectraPaths <- c('./path/file1', './path/file2', './path/file3') # spectraMetadata input_spectraMetadata <- data.frame(matrix(data=c(input_spectraPaths, c('a','b','c')), nrow=3, ncol=2, dimnames=list(c(),c('filepath', 'testcol')), byrow=FALSE), stringsAsFactors=FALSE) # temporary file location spectraMetaPath <- tempfile(pattern="file", tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_spectraMetadata, file=spectraMetaPath, row.names=FALSE) # load data from CSV spectraPaths_and_metadata_UI_helper(spectraPaths = NULL, spectraMetadataPath = spectraMetaPath)## Input data # spectraPath input_spectraPaths <- c('./path/file1', './path/file2', './path/file3') # spectraMetadata input_spectraMetadata <- data.frame(matrix(data=c(input_spectraPaths, c('a','b','c')), nrow=3, ncol=2, dimnames=list(c(),c('filepath', 'testcol')), byrow=FALSE), stringsAsFactors=FALSE) # temporary file location spectraMetaPath <- tempfile(pattern="file", tmpdir=tempdir(), fileext='.csv') # save csv utils::write.csv(input_spectraMetadata, file=spectraMetaPath, row.names=FALSE) # load data from CSV spectraPaths_and_metadata_UI_helper(spectraPaths = NULL, spectraMetadataPath = spectraMetaPath)
TIC accessor
## S4 method for signature 'peakPantheRAnnotation' TIC(object)## S4 method for signature 'peakPantheRAnnotation' TIC(object)
object |
peakPantheRAnnotation |
(float) A numeric vector of Total Ion Chromatogram or NA, of length number of spectra files
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation TIC(annotation) # [1] NA NA NA }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation TIC(annotation) # [1] NA NA NA }
uROI accessor returns targetFeatTable with cpdID, cpdName added
## S4 method for signature 'peakPantheRAnnotation' uROI(object)## S4 method for signature 'peakPantheRAnnotation' uROI(object)
object |
peakPantheRAnnotation |
(data.frame) target feature table with compounds as row and uROI parameters as columns
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation uROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 NA NA NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA NA NA ID-2 Cpd 2 }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID', 'cpdName', 'rtMin', 'rt', 'rtMax', 'mzMin', 'mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) ## default values without annotation uROI(annotation) # rtMin rt rtMax mzMin mz mzMax cpdID cpdName # 1 NA NA NA NA NA NA ID-1 Cpd 1 # 2 NA NA NA NA NA NA ID-2 Cpd 2 }
uROIExist accessor
## S4 method for signature 'peakPantheRAnnotation' uROIExist(object)## S4 method for signature 'peakPantheRAnnotation' uROIExist(object)
object |
peakPantheRAnnotation |
(bool) flag if uROI have been set
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) uROIExist(annotation) # [1] FALSE }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) uROIExist(annotation) # [1] FALSE }
useFIR accessor
## S4 method for signature 'peakPantheRAnnotation' useFIR(object)## S4 method for signature 'peakPantheRAnnotation' useFIR(object)
object |
peakPantheRAnnotation |
(bool) flag if FIR are to be used
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) useFIR(annotation) # [1] FALSE }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) useFIR(annotation) # [1] FALSE }
useUROI accessor
## S4 method for signature 'peakPantheRAnnotation' useUROI(object)## S4 method for signature 'peakPantheRAnnotation' useUROI(object)
object |
peakPantheRAnnotation |
(bool) flag if uROI are to be used
if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) useUROI(annotation) # [1] FALSE }if(requireNamespace('faahKO')){ ## Initialise a peakPantheRAnnotation object with 3 samples and 2 targeted ## compounds # Paths to spectra files library(faahKO) spectraPaths <- c(system.file('cdf/KO/ko15.CDF', package = 'faahKO'), system.file('cdf/KO/ko16.CDF', package = 'faahKO'), system.file('cdf/KO/ko18.CDF', package = 'faahKO')) # targetFeatTable targetFeatTable <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c('cpdID','cpdName','rtMin','rt','rtMax','mzMin','mz', 'mzMax'))), stringsAsFactors=FALSE) targetFeatTable[1,] <- c('ID-1', 'Cpd 1', 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) targetFeatTable[2,] <- c('ID-2', 'Cpd 2', 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) targetFeatTable[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric, FUN.VALUE=numeric(2)) annotation <- peakPantheRAnnotation(spectraPaths=spectraPaths, targetFeatTable=targetFeatTable) useUROI(annotation) # [1] FALSE }