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MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators ofgene expression. In recent years, there has been a tremendous and growinginterest among researchers to investigate the role of miRNAs in normal cellularas well as in disease processes. Thus to investigate the role of miRNAs in oralcancer, we analyse the expression levels of miRNAs to identify miRNAs withstatistically significant differential expression in cancer tissues.  In this article, we propose a novel Bayesian hierarchical model of miRNAexpression data. Compelling evidences have demonstrated that the transcriptionprocess of miRNAs in human genome is a latent process instrumental for theobserved expression levels. We take into account positional clustering of themiRNAs in the analysis and model the latent transcription phenomenonnonparametrically by an appropriate Gaussian process.  For the testing purpose we employ a novel Bayesian multiple testing methodwhere we mainly focus on utilizing the dependence structure between thehypotheses for better results, while also ensuring optimality in many respects.Indeed, our non-marginal method yielded results in accordance with theunderlying scientific knowledge which are found to be missed by the verypopular Benjamini-Hochberg method.

A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering