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No Control Genes Required: Bayesian Analysis of qRT-PCR Data

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  • Mikhail V Matz
  • Rachel M Wright
  • James G Scott

Abstract

Background: Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. Results: In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Conclusions: Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

Suggested Citation

  • Mikhail V Matz & Rachel M Wright & James G Scott, 2013. "No Control Genes Required: Bayesian Analysis of qRT-PCR Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-12, August.
  • Handle: RePEc:plo:pone00:0071448
    DOI: 10.1371/journal.pone.0071448
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    References listed on IDEAS

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    1. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    2. Daijun Ling, 2012. "SASqPCR: Robust and Rapid Analysis of RT-qPCR Data in SAS," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-5, January.
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    1. Francisco Ruiz-Raya & Jose C Noguera & Alberto Velando, 2022. "Light received by embryos promotes postnatal junior phenotypes in a seabird [The evolution of social behavior]," Behavioral Ecology, International Society for Behavioral Ecology, vol. 33(6), pages 1047-1057.
    2. Colette Mair & Sema Nickbakhsh & Richard Reeve & Jim McMenamin & Arlene Reynolds & Rory N Gunson & Pablo R Murcia & Louise Matthews, 2019. "Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.

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