Background: Aging is an organized biological process that is regulated by highly interconnected
pathways between different cells and tissues in the living organism. Identification of
similar genes between tissues in different ages may also help to discover the general mechanism of
aging or to discover more effective therapeutic decisions.
Objective: According to the wide application of model-based clustering techniques, the aim is to
evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid
method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal
Methods: In this study, the expression of aging data from NCBI’s Gene Expression Omnibus was
elaborated to utilize proper data. A set of common genes which were differentially expressed between
different tissues were selected and then clustered together through two methods. Finally, the
biological significance of clusters was evaluated, using their ability to find genes in the cell using
Results: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed
using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are
related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen
complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria,
the cancer development incidence, and the chronic kidney failure, respectively.
Conclusion: Results showed that most biological changes of aging between tissues are related to
the specific components of immune cells. Also, the application of MMVNDs can increase the ability
to find similar genes.