A variable clustering approach for overdispersed high-dimensional count data using a copula-based mixture model

In this paper, we propose a latent variable model for the analysis and clustering of high-dimensional correlated and overdispersed count data. We use a set of random effects to capture within-group correlations and a copula to address between-group correlations. Since maximum likelihood (ML) estimation is computationally intensive for high-dimensional data, we propose a fast moment-based estimation procedure. Additionally, we implement the proposed estimation procedure into a clustering algorithm, borrowing ideas from the K-means algorithm. Based on a simulation study, we demonstrate that the estimation procedure shows good stability and precision. Furthermore, it is computationally fast, unlike the ML approach which shows serious convergence issues. In addition, the clustering algorithm with our moment-based estimation procedure can identify the simulated grouped data structure. The approach is further illustrated with a high-dimensional RNA-Seq dataset of commercial potatoes.