Statistical analysis in metabolic phenotyping

Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping. Metabolomics studies using large-scale NMR or mass spectrometry experiments on biofluids or tissues generate complex data. This protocol provides guidelines and software (supplied in Jupyter notebooks) for the statistical analysis of these data.

Automated summary

Metabolic phenotyping plays a crucial role in translational biomedical research, utilizing advanced analytical technologies such as MS and NMR spectroscopy. This paper proposes an efficient protocol for statistical analysis of metabolic data generated by these methods, offering solutions for various steps in the data analytics workflow. The protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.