Comprehensive LC-MS Metabolomics Data Processing with notame R/Bioconductor Package

Liquid chromatography-mass spectrometry (LC-MS) is widely used in metabolomics. Raw LC-MS data is relatively complex, consisting of molecular features originating not only from unique metabolites but also from redundant adducts, in-source fragments, artifacts, and impurities. It is also prone to signal intensity drift during long sequences, missing values, and false positives in statistical tests. To tackle these instrument-related and data-dependent challenges with robust pre-processing and quality evaluation tools, we have developed the notame R package bundle, which recently became available as a R/Bioconductor release and now supports SummarizedExperiment data format. It pre-processes LC-MS metabolomics data by correcting signal intensity drift, flagging potential low-quality and contaminant features, imputing missing values, and clustering features likely originating from the same metabolite. Most of the functions include default recommended values that can be modified by the user. Univariate and multivariate statistics with parametric and non-parametric alternatives and false discovery rate can be performed with notameStats package. Results and data visualizations, such as quality control figures, PCA, heatmaps, volcano plots, and feature-wise graphs, are available in notameViz package. Together, these packages contribute to a complete metabolomics data analysis workflow, connecting the phases between signal detection/alignment and metabolite identification while producing publication-ready illustrations.