Novel Study of Model-based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process

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Author(s): Farzane Ahmadi, Ali-Reza Abadi, Zahra Bazi, Abolfazl Movafagh*.

Journal Name: Current Aging Science

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Abstract:

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 Distributions (MMVNDs).

Methods: In this study, the expression aging data from NCBI’s Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differently 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 Enricher.

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 MMVNDs can increases the ability to find similar genes.

Keywords: Clustering; Mixtures of Matrix-Variate Normal Distributions; Aging; Time Series

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(E-pub Ahead of Print)
DOI: 10.2174/1874609812666191015140449