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

TL;DR

This paper introduces a Bayesian hierarchical model for miRNA expression analysis that incorporates positional clustering and latent transcription processes, improving detection of significant miRNAs in cancer studies.

Contribution

It presents a novel Bayesian multiple testing approach that leverages dependence among hypotheses and models latent transcription nonparametrically, advancing miRNA differential expression analysis.

Findings

Identified miRNAs with significant differential expression missed by traditional methods.

Demonstrated the effectiveness of the Bayesian approach in aligning with biological knowledge.

Improved detection accuracy in miRNA studies related to oral cancer.

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.