A Copula Model Approach to Identify Differential Expressed Genes

Microarray technology is instrumental in pinpointing differentially expressed genes (DEGs) from the vast number of genes on a DNA molecule. The Spotted cDNA array and the oligonucleotide array are two primary microarray types used for detecting gene expressions, and our focus is on the former array. Various methods have been proposed in the literature to identify DEGs, such as those by Newton et al. (2001) and Mav & Chaganty (2004). In this research, we make use of the Gaussian copula to construct a joint distribution for the red and green intensities in cDNA microarrays. We also incorporate a latent Bernoulli variable to indicate the presence of differential expression and use the EM algorithm to estimate the model parameters. By calculating posterior probabilities and ranking them, we identify DEGs in the analysis of five microarray E. coli samples originally studied in Richmond at al. (1999). Our findings show as expected the "Control" sample has no DEGs, IPTG samples have a few DEGs, and Heat shock samples have many DEGs.

Microarray

Gaussian Copula

Differentially expressed genes

Bayes Model 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2024