title: Estimating marginal properties of quantitative real-time PCR data using nonlinear mixed models.
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.
authors.affiliation: Institute of Biostatistics, Leibniz Universität Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Gerhard
authors.forename: Melanie
authors.initials: M
authors.lastname: Bremer
authors.forename: Christian
authors.initials: C
authors.lastname: Ritz
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D012275:Oryza / Q000235:genetics; D028061:Phosphate Transport Proteins / Q000235:genetics / Q000378:metabolism; D010710:Phosphates / Q000378:metabolism; D060888:Real-Time Polymerase Chain Reaction / Q000379:methods*; D012107:Research Design; D012984:Software
chemical_list: D028061:Phosphate Transport Proteins; D010710:Phosphates
keywords: Delta method; Delta-delta Ct value; Gauss-Hermite quadrature; Hierarchical design; Model averaging