Abstract
Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. However, as yet, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.
Highlights
Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers
There are numerous opportunities for fatal flaws to accumulate from the early biomarker discovery phase leading to the failure of a biomarker discovery project, including, but not limited to, poorly designed, underpowered studies that fail to take account of disease heterogeneity; false discoveries from biological variability or technically induced variability; inappropriate employment of data analysis methods and subsequent incorrect interpretation of findings, leading to over optimistic and misleading preliminary results; a lack of expert knowledge incorporated into the discovery phase; and promotion of discriminating, but not necessarily clinically actionable, candidate biomarkers [4]
An examination of all the issues related to biomarker failures in the discovery phase is beyond the scope of this review, which is presented as a perspective on how data analysis methods in metabolomics may contribute to the failure of biomarker studies
Summary
Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. The starting point of data analysis is deemed to be the metabolomics data matrix that exists after preprocessing (alignment, peak identification, batch correction, etc.) has been carried out. Such preprocessing methods are often platform dependent, and there are many open source software options for the data preprocessing step. An examination of all the issues related to biomarker failures in the discovery phase is beyond the scope of this review, which is presented as a perspective on how data analysis methods in metabolomics may contribute to the failure of biomarker studies. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely
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