Abstract
Despite the increasing popularity and applicability of metabolomics for putative biomarker identification, analysis of the data is challenged by low statistical power resulting from the small sample sizes and large numbers of metabolites and other omics information, as well as confounding demographic and clinical variables. To enhance the statistical power and improve reproducibility of the identified metabolite-based biomarkers, we advocate the use of advanced statistical methods that can simultaneously evaluate the relationship between a group of metabolites and various types of variables including other omics profiles, demographic and clinical data, as well as the complex interactions between them. Accordingly, in this chapter, we describe the method of seemingly unrelated regression that can simultaneously analyze multiple metabolites while controlling the confounding effects of demographic and clinical variables (such as gender, age, BMI, smoking status). We also introduce penalized orthogonal components regression as a screening approach that can handle millions of omics predictors in the model.
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Acknowledgments
This research was partially supported by NIH (R03CA211831, P30CA015704-40) and NSF CAREER award IIS-0844945. The authors gratefully acknowledge the support of the Cancer Care Engineering (CCE) project, a joint effort between the Oncological Sciences Center (Purdue Center for Cancer Research, NCI P30CA23168) in Purdue University Discovery Park and the Indiana University Melvin and Bren Simon Cancer Center (NCI P30CA082709).
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Zhang, D., Zhang, M. (2019). Advanced Statistical Methods for NMR-Based Metabolomics. In: Gowda, G., Raftery, D. (eds) NMR-Based Metabolomics. Methods in Molecular Biology, vol 2037. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9690-2_26
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DOI: https://doi.org/10.1007/978-1-4939-9690-2_26
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