Real Time Annotation"

In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

if (getRversion() >= "3.6") {
  knitr::opts_chunk$set(eval = FALSE)
}
required <- c("faahKO")
if (!all(unlist(lapply(required, function(pkg) requireNamespace(pkg, quietly = TRUE))))) {
  knitr::opts_chunk$set(eval = FALSE)
}

The peakPantheR package is designed for the detection, integration and reporting of pre-defined features in MS files.

The Real Time Annotation is set to detect and integrate multiple compounds in one file at a time.

Using an example dataset, this vignette will:

• Detail the Real Time Annotation concept
• Apply the Real Time Annotation to an example dataset

Real Time Annotation Concept

Real time compound integration is set to:

• process multiple compounds in one file at a time
• load list of expected RT / m/z regions of interest (ROI)
• detect features in each ROI and keep the highest intensity one
• determine peak statistics for each feature
• returns:
  • TIC
  • a table with all detected compounds for that file (row: compound, col: statistic)
  • EIC for each ROI
  • sample acquisition date-time from the mzML metadata
  • save EIC plot to disk

Real Time Annotation Example

We can target two features in a MS spectra file from the faahKO package with peakPantheR_singleFileSearch():

setRepositories(ind=1:4)
install.packages('faahKO')

Input Data

An input spectra is selected:

# hide package load message
library(faahKO)

library(faahKO)
## file paths
input_spectraPath  <- c(system.file('cdf/KO/ko15.CDF', package = "faahKO"))
input_spectraPath

Two targeed features are defined and stored in a table with as columns:

• cpdID (numeric)
• cpdName (character)
• rtMin (sec)
• rtMax (sec)
• rt (sec, optional / NA)
• mzMin (m/z)
• mzMax (m/z)
• mz (m/z, optional / NA)

# targetFeatTable
input_targetFeatTable     <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=F)
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)

input_targetFeatTable     <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID", "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))), stringsAsFactors=F)
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)

Run Single File Annotation

peakPantheR_singleFileSearch() takes as input a singleSpectraDataPath pointing to the file to process and targetFeatTable defining the compounds to integrate:

library(peakPantheR)
annotation <- peakPantheR_singleFileSearch(singleSpectraDataPath = input_spectraPath,
                                           targetFeatTable = input_targetFeatTable,
                                           peakStatistic = TRUE,
                                           verbose = TRUE)

annotation$TIC

annotation$acquTime

annotation$peakTable

pander::pandoc.table(annotation$peakTable, digits = 7)

annotation$curveFit

annotation$ROIsDataPoint

Multiple parameters control the file annotation:

• peakStatistic controls the calculation of additional peak statistics: 'ppm_error', 'rt_dev_sec', 'tailing factor' and 'asymmetry factor'
• plotEICsPath if not NA will save a .png of all ROI EICs at the path provided (expects 'filepath/filename.png' for example). If NA no plot is saved
• getAcquTime controls if sample acquisition date-time must be extracted from the mzML metadata. Acquisition time cannot be extracted from other file formats. The additional file access will impact run time
• FIR if not NULL, defines the Fallback Integration Regions (FIR) to integrate when a feature is not found.
• verbose controls if messages detailing calculation progress, time taken and number of features found (total and matched to targets) must be returned
• ... passes arguments to findTargetFeatures to alter peak-picking parameters (e.g. the curveModel, the sampling or fitting params)

The summary plot generated by plotEICsPath, corresponding to the EICs of each integrated regions of interest is as follow:

knitr::include_graphics("../man/figures/singleFileSearch_EICsPlot.png")

EICs plot: Each panel correspond to a targeted feature, with the EIC extracted on the mzMin, mzMax range found. The red dot marks the RT peak apex, and the red line highlights the RT peakwidth range found (rtMin, rtMax)

See Also

• Getting Started with peakPantheR
• Parallel Annotation

peakPantheR documentation built on May 1, 2019, 10:53 p.m.