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=Volume 9 - 2022

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 high capacity to detect different underlying relationships in experimental datasets and to correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.