KODAMA exploratory analysis in metabolic phenotyping

Maria Mgella Zinga; Ebtesam Abdel-Shafy; Tadele Melak; Alessia Vignoli; Silvano Piazza; Luiz Fernando Zerbini; Leonardo Tenori; Stefano Cacciatore

doi:10.3389/fmolb.2022.1070394

KODAMA exploratory analysis in metabolic phenotyping

Front Mol Biosci. 2023 Jan 17:9:1070394. doi: 10.3389/fmolb.2022.1070394. eCollection 2022.

Authors

Maria Mgella Zinga^{1

2}, Ebtesam Abdel-Shafy^{1

3}, Tadele Melak^{4

5}, Alessia Vignoli^{6

7}, Silvano Piazza⁴, Luiz Fernando Zerbini⁸, Leonardo Tenori^{6

7}, Stefano Cacciatore^{1

9}

Affiliations

¹ Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.
² Department of Medical Parasitology and Entomology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania.
³ National Research Centre, Cairo, Egypt.
⁴ Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy.
⁵ Department of clinical chemistry, University of Gondar, Gondar, Ethiopia.
⁶ Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.
⁷ Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
⁸ Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.
⁹ Institute of Reproductive and Developmental Biology, Imperial College London, London, United Kingdom.

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.

Keywords: KODAMA; clustering; metabolomics; semi-supervised; unsupervised.

Publication types

Review

Grants and funding

This work was supported by the International Centre for Genetic Engineering and Biotechnology (LZ, SC, and SP); the EMPOWER Fellowship Programme (MZ); the ICGEB Arturo Falaschi fellowship (EA-S and TM); and the South African National Research Foundation (NRF) Competitive Support for Unrated Researchers: 138113 (SC).