Metabolomics Data Analysis with TIGER.

Dimensionality reduction and metabolite panel derivation in urinary metabolomics based on Random Forest with Gini index feature selection.

These findings highlight the advantage of a unified, supervised tree-based strategy capable of delivering stable classification and interpretable dimensionality reduction across independent untargeted metabolomics platforms, providing a structured and transferable framework for metabolomics-driven biomarker discovery a...

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...

MeTime: an R package for reproducible longitudinal metabolomics data analysis

Abstract Summary MeTime is an opensource R package for reproducible analysis of longitudinal metabolomics data. It builds upon a central S4 container, metime_analyser, that stores multiple datasets, associated metadata and analysis outputs, enabling unified handling of complex longitudinal studies. Analyses are constru...

MSlineaR: An R Package Assessing Linear Behavior to Improve Quality Assurance and Statistical Robustness in Untargeted Metabolomics.

Mass spectrometry-based untargeted metabolomics analyzes complex biological matrices containing thousands of individual features. Linearity is a key analytical parameter in quantitative mass spectrometry, reflecting proportionality between signal intensity and analyte concentration within a defined range. In untargeted...

University of Birmingham Direct infusion mass spectrometry metabolomics dataset : a benchmark for data processing and quality control

Metabodeconplus—An R Package for Automated Deconvolution and Alignment of 1D NMR Metabolomics Data

Background: In one-dimensional NMR spectra of complex biofluids such as urine and plasma, extensive signal overlap obscures individual metabolite signals. Resolving this overlap by deconvolution is only the first step: turning a set of spectra into a table for subsequent statistical analysis also requires the alignment...