knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Authors: Arnaud Wolfer
## Silently loading all packages library(BiocStyle) library(peakPantheR) library(faahKO) library(pander)
peakPantheR package is designed for the detection, integration and
reporting of pre-defined features in MS files (e.g. compounds, fragments,
The Real Time Annotation is set to detect and integrate multiple compounds in one file at a time. It therefore can be deployed on a LC-MS instrument to integrate a set of pre-defined features (e.g. spiked standards) as soon as the acquisition of a sample is completed.
r Biocpkg("faahKO") raw MS dataset as an example, this vignette
Real time compound integration is set to process multiple compounds in one file at a time.
To achieve this,
In the following example we will target two pre-defined features in a single raw
MS spectra file from the
r Biocpkg("faahKO") package. For more details on the
installation and input data employed, please consult the
Getting Started with peakPantheR vignette.
The path to a MS file from the
r Biocpkg("faahKO") is located and used as
library(faahKO) ## file paths input_spectraPath <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO")) input_spectraPath
Two targeted features (e.g. compounds, fragments, adducts, ...) are defined and stored in a table with as columns:
rt(sec, optional /
mz(m/z, optional /
# 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", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric)
# use pandoc for improved readability 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", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222) input_targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962) input_targetFeatTable[,c(3:8)] <- sapply(input_targetFeatTable[,c(3:8)], as.numeric) rownames(input_targetFeatTable) <- NULL pander::pandoc.table(input_targetFeatTable, digits = 9)
peakPantheR_singleFileSearch() takes as input a
pointing to the file to process and
targetFeatTable defining the features to
integrate. The resulting annotation contains all the fitting and integration
library(peakPantheR) annotation <- peakPantheR_singleFileSearch( singleSpectraDataPath = input_spectraPath, targetFeatTable = input_targetFeatTable, peakStatistic = TRUE, curveModel = 'skewedGaussian', verbose = TRUE)
## acquisition time cannot be extracted from NetCDF files annotation$acquTime
# use pandoc for improved readability pander::pandoc.table(annotation$peakTable, digits = 7)
peakPantheR_singleFileSearch() takes multiple parameters that can alter the
TRUEcalculates additional peak statistics: 'ppm_error', 'rt_dev_sec', 'tailing factor' and 'asymmetry factor'
NAwill save a
.pngof all ROI EICs at the path provided (expects
'filepath/filename.png'for example). If
NAno plot is saved
TRUEthe sample acquisition date-time is extracted from the
mzMLmetadata. Acquisition time cannot be extracted from other file formats. The additional file access will impact run time
NULL, defines the Fallback Integration Regions (FIR) to integrate when a feature is not found.
curveModel, defines the peak-shape model to fit to each EIC. By default, a 'skewedGaussian' model is used. The other alternative is the exponentially modified gaussian 'emgGaussian' model.
TRUEmessages calculation progress, time taken and number of features found (total and matched to targets)
...passes arguments to
findTargetFeaturesto alter peak-picking parameters (e.g. the curveModel, the sampling or fitting parameters)
The summary plot generated by
plotEICsPath, corresponding to the EICs of each
integrated regions of interest is as follow:
EICs plot: Each panel correspond to a targeted feature, with the EIC extracted on the
mzMaxrange found. The red dot marks the RT peak apex, and the red line highlights the RT peakwidth range found (
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.