maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines

Abstract

This paper presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results, and visualizations. maplet’s key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. Availability The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.

Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19

“…Data preprocessing and statistical analysis was performed using the ''maplet'' toolbox for R (Chetnik et al, 2021) (https://github.com/krumsieklab/maplet). The dataset was first reduced to the samples that overlap between metabolomics and proteomics (n = 227), and corrected for age, sex, BMI, and COVID-19 status (yes/no).…”

The landscape of metabolic brain alterations in Alzheimer’s disease

“…Metabolites with nominal p-values < 0.05 in the second model were considered replicated. All analyses were performed using the maplet R package (24).…”

metaboprep: an R package for preanalysis data description and processing

“…Going forward, metaboprep will be developed to address evolving needs, starting with additional functionality to enable direct read-in of the new (since 2021) format datafiles supplied by Metabolon. Perhaps unsurprisingly, given the rapid increase in use of metabolomics data in epidemiology, parallel efforts are being made to improve analytical efficiency, such as the recent release of the R package maplet (Metabolomics Analysis PipeLinE Toolbox; Chetnik et al , 2022 ), and to construct pipelines for combining metabolomic datasets across cohorts ( Viallon et al , 2021 ); any future developments of metaboprep will necessarily be made within this context. Our package does not provide any tools for statistical analysis or downstream interpretation, and therefore, we anticipate that metaboprep will be used in conjunction with complementary tools such as MetaboAnalyst ( Pang et al , 2021 ), which provides a broader set of functions to aid raw MS spectra processing as well as post-analytical biomarker analysis.…”