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A Two-Stage Poisson Model for Testing RNA-Seq Data

Author

Listed:
  • Auer Paul L.

    (Fred Hutchinson Cancer Research Center)

  • Doerge Rebecca W

    (Purdue University)

Abstract

RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.

Suggested Citation

  • Auer Paul L. & Doerge Rebecca W, 2011. "A Two-Stage Poisson Model for Testing RNA-Seq Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, May.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:26
    DOI: 10.2202/1544-6115.1627
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    Cited by:

    1. Cui Shiqi & Ji Tieming & Li Jilong & Cheng Jianlin & Qiu Jing, 2016. "What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(2), pages 87-105, April.
    2. Pounds Stanley B. & Gao Cuilan L. & Zhang Hui, 2012. "Empirical Bayesian Selection of Hypothesis Testing Procedures for Analysis of Sequence Count Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-32, October.
    3. Mélina Gallopin & Andrea Rau & Florence Jaffrézic, 2013. "A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    4. Thanh Nguyen & Asim Bhatti & Samuel Yang & Saeid Nahavandi, 2016. "RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.

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