Data in support of enhancing metabolomics research through data mining

Ibon Martínez-Arranz; Rebeca Mayo; Miriam Pérez-Cormenzana; Itziar Mincholé; Lorena Salazar; Cristina Alonso; José M Mato

doi:10.1016/j.dib.2015.02.008

Data in support of enhancing metabolomics research through data mining

Data Brief. 2015 Feb 27:3:155-64. doi: 10.1016/j.dib.2015.02.008. eCollection 2015 Jun.

Authors

Ibon Martínez-Arranz¹, Rebeca Mayo¹, Miriam Pérez-Cormenzana¹, Itziar Mincholé¹, Lorena Salazar², Cristina Alonso¹, José M Mato³

Affiliations

¹ OWL, Parque Tecnológico de Bizkaia, Derio, Bizkaia, Spain.
² Osarten kooperatiba elkartea, Mondragón, Guipúzcoa, Spain.
³ CIC bioGUNE, CIBERehd, Parque Tecnológico de Bizkaia, Derio, Bizkaia, Spain.

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.