KODAMA exploratory analysis in metabolic phenotyping

Abstract

KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.

MetChem: a new pipeline to explore structural similarity across metabolite modules

“…Here, we present MetChem , a new R package built on the KODAMA, an unsupervised machine learning algorithm for dimensionality reduction that has been successfully applied for clustering identification ( Cacciatore et al , 2014 , Zinga et al , 2023 ). MetChem is designed to provide metabolomics researchers with a user-friendly pipeline able to investigate features in metabolic modules defined by their structural similarity.…”