Getting Started with the peakPantheR package"

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)
}

peakPantheR

Package for Peak Picking and ANnoTation of High resolution Experiments in R, implemented in R and Shiny

Overview

peakPantheR implements functions to detect, integrate and report pre-defined features in MS files. It is designed for:

• Real time feature detection and integration (see Real Time Annotation)
  • process multiple compounds in one file at a time
• Post-acquisition feature detection, integration and reporting (see Parallel Annotation)
  • process multiple compounds in multiple files in parallel, store results in a single object

Installation

Install the development version of the package directly from GitHub with:

# Install devtools
if(!require("devtools")) install.packages("devtools")
devtools::install_github("phenomecentre/peakPantheR")

If the dependencies mzR and MSnbase are not successfully installed, Bioconductor must be added to the default repositories with:

setRepositories(ind=1:2)

Input Data

Both real time and parallel compound integration require a common set of information:

• Path(s) to netCDF / mzML MS file(s)
• An expected region of interest (RT / m/z window) for each compound.

The faahKO package provides with a set of MS spectra that we can use for peakPantheR annotation and is installed as follow:

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

# hide package load message
library(faahKO)

library(faahKO)
## file paths
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"))

library(faahKO)
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"))
input_spectraPaths

A table of targeted features contains as column:

• 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(1, "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c(2, "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(1,3:8)] <- sapply(input_targetFeatTable[,c(1,3:8)], 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(1, "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
input_targetFeatTable[2,] <- c(2, "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
input_targetFeatTable[,c(1,3:8)] <- sapply(input_targetFeatTable[,c(1,3:8)], as.numeric)
rownames(input_targetFeatTable) <- NULL
pander::pandoc.table(input_targetFeatTable, digits = 9)

See Also

• Real Time Annotation
• Parallel Annotation

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