AUTHOR=Zinga Maria Mgella , Abdel-Shafy Ebtesam , Melak Tadele , Vignoli Alessia , Piazza Silvano , Zerbini Luiz Fernando , Tenori Leonardo , Cacciatore Stefano TITLE=KODAMA exploratory analysis in metabolic phenotyping JOURNAL=Frontiers in Molecular Biosciences VOLUME=9 YEAR=2023 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.1070394 DOI=10.3389/fmolb.2022.1070394 ISSN=2296-889X 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.