MetaboCensoR: A Shiny Application for Data Filtering in Untargeted LC-MS Metabolomics to Enhance Interpretability

Abstract

Untargeted LC-MS metabolomics datasets often contain large numbers of redundant and non-informative features arising from background contaminants, multiple ion forms, poorly integrated peaks, and other low-quality signals. These features complicate downstream analysis by inflating feature space, degrading molecular networks, impeding pathway analysis, and obscuring statistically meaningful changes. Here, we present MetaboCensoR, an input-versatile Shiny application and local R package that bridges the gap between LC-MS peak picking and downstream interpretation through analyte-centric peak table filtering. The workflow integrates four fully interactive modules for blank, ion-species, quality-control, and peak-based filtering, with synchronized processing of associated spectra files. The tool was evaluated across three independent datasets covering plant extracts, human cell lines, and bacterial interactions. Across these case studies, data filtering reduced feature redundancy and improved downstream interpretation in feature-based molecular networking, pathway-level functional analysis, and differential abundance testing, while retaining known analytes. MetaboCensoR was systematically benchmarked against existing tools, demonstrating comparable ion-species annotation coverage and a favorable overall balance of target recovery, adduct precision, and feature reduction. Together, these evaluations support MetaboCensoR as a robust approach for systematic peak table curation, enhancing interpretability and analytical value of untargeted metabolomics data.