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Identifying atypically expressed chromosome regions using RNA-Seq data

Author

Listed:
  • Vinícius Diniz Mayrink

    (ICEx Universidade Federal de Minas Gerais, Av. Antônio Carlos)

  • Flávio B. Gonçalves

    (ICEx Universidade Federal de Minas Gerais, Av. Antônio Carlos)

Abstract

The number of studies dealing with RNA-Seq data analysis has experienced a fast increase in the past years making this type of gene expression a strong competitor to the DNA microarrays. This paper proposes a Bayesian model to detect low and highly-expressed chromosome regions using RNA-Seq data. The methodology is based on a recent work designed to detect highly-expressed (overexpressed) regions in the context of microarray data. A hidden Markov model is developed by considering a mixture of Gaussian distributions with ordered means in a way that first and last mixture components are supposed to accommodate the under and overexpressed genes, respectively. The model is flexible enough to efficiently deal with the highly irregular spaced configuration of the data by assuming a hierarchical Markov dependence structure. The analysis of four cancer data sets (breast, lung, ovarian and uterus) is presented. Results indicate that the proposed model is selective in determining the expression status, robust with respect to prior specifications and provides tools for a global or local search of under and overexpressed chromosome regions.

Suggested Citation

  • Vinícius Diniz Mayrink & Flávio B. Gonçalves, 2020. "Identifying atypically expressed chromosome regions using RNA-Seq data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 619-649, September.
    • Handle: RePEc:spr:stmapp:v:29:y:2020:i:3:d:10.1007_s10260-019-00496-4
    DOI: 10.1007/s10260-019-00496-4
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    References listed on IDEAS

    as
    1. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
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    4. Panagiotis Papastamoulis & Magnus Rattray, 2018. "A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 3-23, January.
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    6. Vinícius Diniz Mayrink & Flávio Bambirra Gonçalves, 2017. "A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 387-412, February.
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