Title: A Novel Bayesian Multiple Testing Approach to Deregulated miRNA Discovery Harnessing Positional Clustering,
Abstract: MicroRNAs (miRNAs) are 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
where 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.