Zinga Maria Mgella, Abdel-Shafy Ebtesam, Melak Tadele, Vignoli Alessia, Piazza Silvano, Zerbini Luiz Fernando, Tenori Leonardo, Cacciatore Stefano
Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.
Department of Medical Parasitology and Entomology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania.
Front Mol Biosci. 2023 Jan 17;9:1070394. doi: 10.3389/fmolb.2022.1070394. eCollection 2022.
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.
KODAMA是代谢组学研究中用于进行探索性分析的宝贵工具。常用于代谢表型分析的先进分析技术，如质谱和核磁共振光谱，会产生大量高维数据。这些复杂的数据集需要量身定制的统计分析，以便能够从嘈杂的背景中突出潜在有趣的模式。因此，用于探索性分析的代谢组学数据可视化围绕降维展开。KODAMA擅长揭示高维数据（如代谢组学数据）中的局部结构。KODAMA具有很高的能力来检测实验数据集中不同的潜在关系，并将提取的特征与相关元数据相关联。在此，我们描述了KODAMA探索性分析在代谢组学研究中的主要应用。
