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Estimating marginal properties of quantitative real‐time PCR data using nonlinear mixed models
Author(s) -
Gerhard Daniel,
Bremer Melanie,
Ritz Christian
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12124
Subject(s) - nonlinear system , computer science , set (abstract data type) , data set , mixed model , model selection , selection (genetic algorithm) , focus (optics) , population , statistics , expression (computer science) , mathematics , data mining , machine learning , physics , demography , quantum mechanics , sociology , optics , programming language
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 Δ Δ c ( 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.