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Abstract:
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Existing approaches to personalized medicine rely on molecular data analyses across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals. We previously developed a framework, N-of-1-pathways, for single-subject mRNA expression data analysis. N-of-1-pathways quantifies and tests the statistical significance of differential pathway (gene set) expression using a pair of samples derived from a single patient under two conditions (e.g., unaffected tissue vs. tumor tissue). Here, we study operating characteristics (empirical size and power) for pathway testing using statistical methods pertinent to the N-of-1-pathways scenario. These include a basic, nonparametric, paired-sample test (the Wilcoxon signed-rank test), and also manipulation of the Mahalanobis distance of paired-sample gene expression points from a null-effect response line. We explore the methods for identifying differentially expressed pathways (DEPs) across various effect sizes, pathway sizes, and distributional assumptions on the mRNA expression. Lastly, we illustrate our approach with an application to cancer RNA-seq data sets.
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