Abstract
Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this ‘omic’ discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled “Enhancing metabolomics research through data mining”, which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.
Highlights
Metabolomics research has evolved considerably, during the last decade
The future of metabolomics will depend on its capability to find biomarkers
We focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics
Summary
The advantages of using both univariate and multivariate approaches in data mining have been recently reviewed [6]. Both approaches are complementary and their results do not necessarily coincide. Following the advice to combine the use of both univariate and multivariate approaches, we have developed a web application. This is expected to help with the visualization and interpretation of the data analyses
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