Bayesian mixture model averaging for identifying the different gene expressions of chickpea (Cicer arietinum) plant tissue

Ani Budi Astuti, Nur Iriawan, Irhamah and Heri Kuswanto

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 21, 10564-10581

Abstract: Identification of different gene expressions of chickpea (Cicer arietinum) plant tissue is needed in order to develop new varieties of chickpea plant which is resistant to disease through the insertion of genes. This plant is the third legume plant of the Leguminosae (Fabaceae) family and is much needed in the world due to its high-protein seeds and roots that contain symbiotic nitrogen-fixing bacteria. This paper has succeeded to demonstrate the work of Bayesian mixture model averaging (BMMA) approach to identify the different gene expressions of chickpea plant tissue in Indonesia. The results show that the best BMMA normal models contain from 727 (73%) up to 939 (94%) models from 1,000 generated mixture normal models. The fitted BMMA models to gene expression differences data on average is 0.2878511 for Kolmogorov–Smirnov (KS) and 0.1278080 for continuous rank probability score (CRPS). Based on these BMMA models, there are three groups of gene IDs: downregulated, regulated, and upregulated. The results of this grouping can be useful to find new varieties of chickpea plants that are more resistant to disease. The BMMA normal models coupled with Occam's window as a data-driven modeling have succeed to demonstrate the work of building the gene expression differences microarray experiments data.

Date: 2017
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DOI: 10.1080/03610926.2016.1239112

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