Document Type

Article

Publication Date

2025

DOI

10.3390/analytics4020011

Publication Title

Analytics

Volume

4

Issue

2

Pages

11 (1-21)

Abstract

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian copula model to characterize the joint distribution of red and green intensities, effectively capturing their dependence structure. Given the right-skewed nature of the intensity distributions, we model the marginal distributions using gamma distributions. Differentially expressed genes are identified using the Bayes estimate under our proposed copula framework. To evaluate the performance of our model, we conduct simulation studies to assess parameter estimation accuracy. Our results demonstrate that the proposed approach outperforms existing methods reported in the literature. Finally, we apply our model to Escherichia coli microarray data, illustrating its practical utility in gene expression analysis.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Restrictions apply to the availability of these data. Data are available from ref. [16]."

Original Publication Citation

Liyanaarachchi, P., & Chaganty, N. R. (2025). Copula-based Bayesian model for detecting differential gene expression. Analytics, 4(2), 1-21, Article 11. https://doi.org/10.3390/analytics4020011

ORCID

0000-0003-2325-3513 (Chaganty)

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