Statistical significance analysis of nuclear magnetic resonance-based metabonomics data
Section snippets
Data collection
The urine and fecal data used for the data analysis were collected in a previously published study by Romick-Rosendale and coworkers [27]. PCA of urine, as well as fecal extracts, of control and antibiotic-treated mice showed minimal or no overlap in PCA scores plots, providing a near ideal test data set for demonstration of the statistical significance analysis algorithm outlined in the article.
Statistical significance analysis
Bucket tables were generated using AMIX (Bruker Biospin, Billerica, MA, USA). NMR spectra were
Decision tree algorithm
The first step in developing a rigorous statistical significance analysis approach for NMR-based metabonomics data is to determine the appropriate tests to obtain P scores for buckets corresponding to PCA loadings plot points. Due to the nature of NMR-based metabonomics data, which can involve small numbers of subjects in pilot studies, different numbers of subjects in control and treated/disease groups, and data for which the variance is generally different for each group—all of which impact
Conclusion
The ultimate goal of NMR-based metabonomics research is discovery of human disease biomarkers and translation to clinical use. Although the technology shows substantial promise, hardly any human disease biomarkers discovered using NMR-based metabonomics have been translated to clinical use at this time. One substantial obstacle is that multivariate statistical PCA-driven methods widely used by investigators conducting basic metabonomics research do not routinely subject data to rigorous
Acknowledgments
The authors acknowledge financial support from Bruker Biospin that enabled this study. M.A.K. acknowledges support from the Ohio Eminent Scholar program, Ohio Board of Regents, and Miami University for support that enabled establishing and outfitting the Eminent Scholar laboratory with the high field NMR instrumentation used in this study.
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