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.
| Original language | English |
|---|---|
| Journal | Biometrics |
| Volume | 70 |
| Issue number | 1 |
| Pages (from-to) | 247-254 |
| Number of pages | 8 |
| ISSN | 0006-341X |
| DOIs | |
| Publication status | Published - Mar 2014 |