peakTrAMS R Package

peakTrAMS is an R package designed for peak detection, subtraction, and comparison of mass spectrometry data from HILIC and RP chromatographic methods. This README provides a step-by-step guide for running a typical workflow using the peakTrAMS package.

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Installation

To use the peakTrAMS package, install it as follows:

Install R language and ProteoWizard and place the installation path to the global environment variables. Detailed instructions: You can do that by right click on My computer>Advanced system settings>Environment Variables>Variable Path>Edit and add the paths for example: C:\Program Files\R\R-4.4.2\bin and C:\Users\xxxxx\AppData\Local\Apps\ProteoWizard xxxx. You have successfully implemented if you can access R and msconvert from commandline (windows key+R>type cmd. A command line prompt will appear. If someone types R, then R must start. Same with msconvert.

Install mzR, xcms and devtools Detailed instructions: R>if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") R>BiocManager::install("mzR") R>BiocManager::install("xcms") R>install.packages("devtools")

library("devtools")
install_github("nalygizakis/peakTrAMS")
library(peakTrAMS)

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Workflow Overview

1. Specify Paths for Raw Files

Define the paths to the raw .mzXML files for HILIC and RP samples and blanks:

path_HILIC <- list.files(paste(find.package(package = "peakTrAMS"), "data", sep = "/"), 
                         pattern = c("Sample_HILIC", ".mzXML"), full.names = TRUE)

path_RP <- list.files(paste(find.package(package = "peakTrAMS"), "data", sep = "/"), 
                      pattern = c("Sample_RP", ".mzXML"), full.names = TRUE)

path_Blank_HILIC <- list.files(paste(find.package(package = "peakTrAMS"), "data", sep = "/"), 
                               pattern = c("Blank_HILIC", ".mzXML"), full.names = TRUE)

path_Blank_RP <- list.files(paste(find.package(package = "peakTrAMS"), "data", sep = "/"), 
                            pattern = c("Blank_RP", ".mzXML"), full.names = TRUE)

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2. Load Library

library("peakTrAMS")

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3. Read, Calibrate mzXML, and Restrict Processing Area

Create an output directory and calibrate the raw files:

dir.create("output", showWarnings = TRUE)
setwd(file.path(getwd(), "output"))

sample_RP <- calibration(mzXML = read.mzXML(filename = path_RP),
                         calibration_region = c(0.08, 0.26), 
                         calibrant_substance = "Na_Formate_pos", 
                         int_thres = 1000)

blank_RP <- calibration(read.mzXML(filename = path_Blank_RP),
                        calibration_region = c(0.08, 0.26), 
                        calibrant_substance = "Na_Formate_pos", 
                        int_thres = 1000)

sample_HILIC <- calibration(read.mzXML(path_HILIC), 
                            calibrant_substance = "Na_Formate_pos",
                            calibration_region = c(0.08, 0.26), 
                            int_thres = 1000)

blank_HILIC <- calibration(mzXML = read.mzXML(path_Blank_HILIC),
                           calibration_region = c(0.08, 0.26), 
                           calibrant_substance = "Na_Formate_pos", 
                           int_thres = 1000)

Restrict the scans based on retention time:

sample_HILIC <- removescans(sample_HILIC, scansORtime = c("beginning", 0.26), time = TRUE)
blank_HILIC <- removescans(blank_HILIC, scansORtime = c("beginning", 0.26), time = TRUE)

sample_RP <- removescans(sample_RP, scansORtime = c("beginning", 0.26), time = TRUE)
blank_RP <- removescans(blank_RP, scansORtime = c("beginning", 0.26), time = TRUE)

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4. Scan-by-Scan Subtraction

Subtract blank samples from the corresponding raw samples:

sub_HILIC <- subtract(sample = sample_HILIC, blank = blank_HILIC, mzdiff = 0.01, filter = 100)
sub_RP <- subtract(sample = sample_RP, blank = blank_RP, mzdiff = 0.01, filter = 100)

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5. Write Subtracted Chromatograms

Save the subtracted chromatograms to .mzXML files:

write.mzXML(sub_RP, "Subtracted_RP.mzXML", precision = "64")
write.mzXML(sub_HILIC, "Subtracted_HILIC.mzXML", precision = "64")

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6. Peak Picking and Comparing Peak Lists

Perform peak picking and compare peaks between HILIC and RP:

output <- compareRPHILIC(HILIC = paste(getwd(), "Subtracted_HILIC.mzXML", sep = "/"),
                         RP = paste(getwd(), "Subtracted_RP.mzXML", sep = "/"), 
                         mzthr = 0.01, doubleckeck = TRUE, 
                         ppm = 30, peakwidth = c(14.34, 50), sn = 10)

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7. Prioritization of Common and Uncommon Peaks

Prioritize common and unique peaks:

prioritized_output <- prioritization(output)

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8. Plot Extracted Ion Chromatograms (EICs)

Generate plots for prioritized peaks:

writeoutput(prioritized_output, sampleRP = sub_RP, sampleHILIC = sub_HILIC, type = "o")

This will create PDF files containing the EICs of unique and common peaks.

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Outputs

• Subtracted_RP.mzXML and Subtracted_HILIC.mzXML: Subtracted chromatograms.
• PDFs with unique peaks in HILIC and RP as well as prioritized common peaks.

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Notes

• Ensure the .mzXML files are formatted correctly.
• Adjust parameters (mzdiff, ppm, peakwidth, etc.) as needed for your dataset.

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