Session: Analytical Division Poster:

Metabolomics is a cutting-edge and interdisciplinary area that integrates knowledge of instrumentation, data science, and biochemistry. Briefly, Metabolomics is an area that simultaneously studies the changes in a large number of metabolites using analytical platforms after various treatments. However, the data interpretation for metabolomics data especially studies with various groups is still not well investigated. The principal component analysis (PCA) is one of the most popular machine learning methods in metabolomics analysis, however, the loadings of PCA are not well applied in metabolomics studies. In the study, we developed a new method that can integrate the PCA loading values with the commonly used statistical t-test analysis in a newly designed volcano plot. The study is also designed for researchers to use the PCA model to provide suggestions for metabolites with potential family wised error in a statistical test. The method has been tested in an example study of sea anemones with six different concentrations of atrazine treatment. The results potentially provide a fast but powerful approach to analyze metabolomics data with multiple groups with visualized results.

Lin Jiang

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