A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering
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MicroRNAs (miRNAs) are endogenous, small non-coding RNAs that function as regulators of gene expression. In recent years, there has been a tremendous and growing interest among researchers to investigate the role of miRNAs in normal cellular as well as in disease processes. Thus to investigate the role of miRNAs in oral cancer, we analyse the expression levels of miRNAs to identify miRNAs with statistically significant differential expression in cancer tissues. In this article, we propose a novel Bayesian hierarchical model of miRNA expression data. Compelling evidences have demonstrated that the transcription process of miRNAs in human genome is a latent process instrumental for the observed expression levels. We take into account positional clustering of the miRNAs in the analysis and model the latent transcription phenomenon nonparametrically by an appropriate Gaussian process. For the testing purpose we employ a novel Bayesian multiple testing method to identify the differentially expressed miRNAs which are actually statistically significant. Most of the existing multiple testing methods focus on the validity or admissibility of the procedure whereas exploiting the dependence structure between the hypotheses may often yield much closer to truth inference. In our methodology we mainly focus on utilizing the dependence structure between the hypotheses for better results, while also ensuring optimality in many respects. Indeed, our non-marginal method yielded results in accordance with the underlying scientific knowledge which are found to be missed by the very popular Benjamini-Hochberg method.
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