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A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data

Author

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  • Lin Zhixiang

    (Department of Statistics, Stanford University, Stanford, CA 94305, USA Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA)

  • Li Mingfeng

    (Department of Neurobiology, Kavli Institute for Neuroscience, Yale School of Medicine, 06510 New Haven, CT, USA)

  • Sestan Nenad

    (Department of Neurobiology, Kavli Institute for Neuroscience, Yale School of Medicine, 06510 New Haven, CT, USA)

  • Zhao Hongyu

    (Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA Department of Genetics, Yale School of Medicine, New Haven, Connecticut 06520, USA)

Abstract

The statistical methodology developed in this study was motivated by our interest in studying neurodevelopment using the mouse brain RNA-Seq data set, where gene expression levels were measured in multiple layers in the somatosensory cortex across time in both female and male samples. We aim to identify differentially expressed genes between adjacent time points, which may provide insights on the dynamics of brain development. Because of the extremely small sample size (one male and female at each time point), simple marginal analysis may be underpowered. We propose a Markov random field (MRF)-based approach to capitalizing on the between layers similarity, temporal dependency and the similarity between sex. The model parameters are estimated by an efficient EM algorithm with mean field-like approximation. Simulation results and real data analysis suggest that the proposed model improves the power to detect differentially expressed genes than simple marginal analysis. Our method also reveals biologically interesting results in the mouse brain RNA-Seq data set.

Suggested Citation

  • Lin Zhixiang & Li Mingfeng & Sestan Nenad & Zhao Hongyu, 2016. "A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(2), pages 139-150, April.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:2:p:139-150:n:3
    DOI: 10.1515/sagmb-2015-0070
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    References listed on IDEAS

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    1. Min Chen & Judy Cho & Hongyu Zhao, 2011. "Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 7(4), pages 1-13, April.
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