Real Time Annotation

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

Package: r Biocpkg("peakPantheR")
Authors: Arnaud Wolfer

## Silently loading all packages
library(BiocStyle)
library(peakPantheR)
library(faahKO)
library(pander)

Introduction

The peakPantheR package is designed for the detection, integration and reporting of pre-defined features in MS files (e.g. compounds, fragments, adducts, ...).

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.

Using the r Biocpkg("faahKO") raw MS dataset as an example, this vignette will:

Abbreviations

Real Time Annotation Concept

Real time compound integration is set to process multiple compounds in one file at a time.

To acheive this, peakPantheR will:

Real Time Annotation Example

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.

Input Data

The path to a MS file from the r Biocpkg("faahKO") is located and used as input spectra:

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:

# 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)

Run Single File Annotation

peakPantheR_singleFileSearch() takes as input a singleSpectraDataPath pointing to the file to process and targetFeatTable defining the features to integrate. The resulting annotation contains all the fitting and integration properties:

library(peakPantheR)
annotation <- peakPantheR_singleFileSearch(
                                    singleSpectraDataPath = input_spectraPath,
                                    targetFeatTable = input_targetFeatTable,
                                    peakStatistic = TRUE,
                                    verbose = TRUE)
annotation$TIC
## acquisition time cannot be extracted from NetCDF files
annotation$acquTime
annotation$peakTable
# use pandoc for improved readability
pander::pandoc.table(annotation$peakTable, digits = 7)
annotation$curveFit
annotation$ROIsDataPoint

peakPantheR_singleFileSearch() takes multiple parameters that can alter the file annotation:

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



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peakPantheR documentation built on April 29, 2020, 5:23 a.m.