KODAMA exploratory analysis in metabolic phenotyping.
KODAMA
clustering
metabolomics
semi-supervised
unsupervised
Journal
Frontiers in molecular biosciences
ISSN: 2296-889X
Titre abrégé: Front Mol Biosci
Pays: Switzerland
ID NLM: 101653173
Informations de publication
Date de publication:
2022
2022
Historique:
received:
14
10
2022
accepted:
28
12
2022
entrez:
3
2
2023
pubmed:
4
2
2023
medline:
4
2
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36733493
doi: 10.3389/fmolb.2022.1070394
pii: 1070394
pmc: PMC9887019
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1070394Commentaires et corrections
Type : ErratumIn
Informations de copyright
Copyright © 2023 Zinga, Abdel-Shafy, Melak, Vignoli, Piazza, Zerbini, Tenori and Cacciatore.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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