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A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data

Author

Listed:
  • Follestad Turid

    (Norwegian University of Science and Technology)

  • Jørstad Tommy S

    (Norwegian University of Science and Technology)

  • Erlandsen Sten E

    (Norwegian University of Science and Technology)

  • Sandvik Arne K

    (Norwegian University of Science and Technology & St. Olav’s University Hospital)

  • Bones Atle M

    (Norwegian University of Science and Technology)

  • Langaas Mette

    (Norwegian University of Science and Technology)

Abstract

We present a Bayesian hierarchical model for quantitative real-time polymerase chain reaction (PCR) data, aiming at relative quantification of DNA copy number in different biological samples. The model is specified in terms of a hidden Markov model for fluorescence intensities measured at successive cycles of the polymerase chain reaction. The efficiency of the reaction is assumed to depend on the abundance of the target DNA through fluorescence intensities, and the relationship is specified based on the kinetics of the reaction. The model incorporates the intrinsic random nature of the process as well as measurement error. Taking a Bayesian inferential approach, marginal posterior distributions of the quantities of interest are estimated using Markov chain Monte Carlo. The method is applied to simulated data and an experimental data set.

Suggested Citation

  • Follestad Turid & Jørstad Tommy S & Erlandsen Sten E & Sandvik Arne K & Bones Atle M & Langaas Mette, 2010. "A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-21, January.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:3
    DOI: 10.2202/1544-6115.1427
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    References listed on IDEAS

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    1. Lalam Nadia, 2007. "Statistical Inference for Quantitative Polymerase Chain Reaction Using a Hidden Markov Model: A Bayesian Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-36, March.
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