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Estimating marginal properties of quantitative real-time PCR data using nonlinear mixed models
Daniel Gerhard1, Melanie Bremer, Christian Ritz
1Institute of Biostatistics, Leibniz Universität Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany.
Abstract:
A unified modeling framework based on a set of nonlinear mixed models is proposed for flexible modeling of gene expression in real-time PCR experiments. Focus is on estimating the marginal or population-based derived parameters: cycle thresholds and ΔΔc(t), but retaining the conditional mixed model structure to adequately reflect the experimental design. Additionally, the calculation of model-average estimates allows incorporation of the model selection uncertainty. The methodology is applied for estimating the differential expression of a phosphate transporter gene OsPT6 in rice in comparison to a reference gene at several states after phosphate resupply. In a small simulation study the performance of the proposed method is evaluated and compared to a standard method.
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