Estimating marginal properties of quantitative real‐time PCR data using nonlinear mixed models. Issue 1 (3rd December 2013) - Gerhard, Daniel; Bremer, Melanie; Ritz, Christian
Summary 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 <alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgg4ss8gc7h" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" />ΔΔc(t)</alternatives>, 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.