Metabolomics is the systematic study of the unique chemical fingerprints that specific cellular processes leave behind, and it is a valuable source in early disease diagnosis process. Human urine recently became a popular medium for metabolomics biomarker discovery because its collection is non-invasive. Depending on the amount of water a patient drink, sometimes renal dilution of urine can be problematic in this type of urinary biomarker analysis. Currently various normalization techniques such as creatinine ratio, osmolality, specific gravity, dry mass, urine volume, and area under the curve are used to account for the renal dilution. However, these normalization techniques have their own drawbacks. In this project, LC/MS/MS urinary metabolomic data obtained from prostate cancer (n=56), bladder cancer (n=57) and control (n=69) groups were analyzed using statistical normalization techniques. The normalization techniques investigated in this study are Creatinine Ratio, Linear Baseline, Cyclic Loess, Quantile, Probabilistic Quotient, Auto Scaling, Pareto Scaling, and Variance Stabilizing Normalization. The appropriate summary statistics for comparison of normalization techniques were created using variances, coefficients of variation, and boxplots. For each normalization technique, a principal component analysis was performed to identify clusters based on cancer type.  In addition, hypothesis tests were conducted to determine if the normalized biomarkers could be used to differentiate between the cancer types.  The detailed experimental conditions and results will be presented at the conference.

Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data

2019 Fall National Meeting

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