PipMet (Legacy Version)

Note: This repository contains the original version of the PipMet package developed during 2021-2022. For the latest stable version, updated documentation, and the official release associated with the 2026 publication, please visit the official repository: github.com/PipMet/PipMet.

Publication

This work is part of the research published in: *Brenelli, T. L., Couto, A. C. F., Aricetti, J.. (2026). PipMet: Pipeline for processing GC-MS Metabolomics data and statistical graphics visualization [Link] (https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15000601/v2)

The PipMet package was developed to perform end-to-end processing of metabolomic-based GC-MS data, with automated generation of high-quality figures throughout the workflow. All user inputs are obtained through pop-up windows.

Installation

Dependencies installation

Before installing PipMet, make sure that all required dependencies are installed.
The following code will automatically check for and install all necessary Bioconductor and CRAN packages:

# Install BiocManager if necessary
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install Bioconductor dependencies
BiocManager::install(c(
  "xcms", "MSnbase", "CluMSID", "metaMS", "BiocParallel",
  "Biobase", "ProtGenerics", "CAMERA", "NormalyzerDE"
), ask = FALSE, update = TRUE)

# Install CRAN dependencies
cran_pkgs <- c("svDialogs", "pheatmap", "ddpcr", "webchem", "fritools", "pracma")
installed <- cran_pkgs %in% rownames(installed.packages())
if (any(!installed)) install.packages(cran_pkgs[!installed])

PipMet installation

You can install the released version of PipMet from GitHub with:

devtools::install_github("AnnafCouto/PipMet")

The user may also install the package through Bioconductor repository:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("PipMet")

Example

The package is constituted of two main functions with pre-set parameters and algorithms for GC-MS data processing. The workData() function reads, treats and process GC-MS sample data, with metabolite identification and quantification. The second one, workLib() provides a workflow for an internal library creation to be uploaded into NIST MS Search software for spectra annotation.

The package was thought to be as friendly-user as possible. Therefore, when information is needed, pop-ups will appear to collect input.

library(PipMet)
result <- workData(
   sample_dir = system.file("extdata", package = "PipMet"),
   metadata = system.file("extdata", "metadata.csv", package = "PipMet"),
   extension = ".mzXML",
   myDir = '~/',
   example = TRUE,
   pictures = TRUE
)

library(PipMet)
workLib(
   extension = ".mzML",
   myDir = '~/',
   example = TRUE,
)

Set ‘pictures = TRUE’ to generate pictures throughout the code.

For more information, see the package vignette.

License

This project is licensed under the GPL-3 License. Copyright (c) 2021-2022 Anna Couto and CNPEM.