Correction of Preprocessed MS Data

An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) ). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow.

mpactr

Overview

mpactr is a collection of filters for the purpose of identifying high quality MS1 features by correcting peak selection errors introduced during the pre-processing of tandem mass spectrometry data.

Filters in this package address the following issues:

• filter_mispicked_ions(): removal of mispicked peaks, or those isotopic patterns that are incorrectly split during preprocessing.
• filter_group(): removal of features overrepresented in a specific group of samples; for example removal of features present in solvent blanks due to carryover between samples.
• filter_cv(): removal of non-reproducible features, or those that are inconsistent between technical replicates.
• filter_insource_ions(): removal of fragment ions created during the first ionization in the tandem MS/MS workflow.

All filters are independent, meaning they can be used to create a project-specific workflow, or you can learn more in the Getting Started page.

Installation

You can install the CRAN version with:

install.packages("mpactr")

You can install the development version of mpactr from GitHub with:

# install.packages("devtools")
devtools::install_github("mums2/mpactr")

Get started

See the Getting Started page to get started.

Getting help

If you encounter an issue, please file an issue on GitHub. Please include a minimal reproducible example with your issue.

Contributing

Is there a feature you’d like to see included, please let us know! Pull requests are welcome on GitHub.