Follestad Turid, Jørstad Tommy S, Erlandsen Sten E, Sandvik Arne K, Bones Atle M, Langaas Mette
Norwegian University of Science and Technology.
Stat Appl Genet Mol Biol. 2010;9:Article 3. doi: 10.2202/1544-6115.1427. Epub 2010 Jan 6.
We present a Bayesian hierarchical model for quantitative real-time polymerase chain reaction (PCR) data, aiming at relative quantification of DNA copy number in different biological samples. The model is specified in terms of a hidden Markov model for fluorescence intensities measured at successive cycles of the polymerase chain reaction. The efficiency of the reaction is assumed to depend on the abundance of the target DNA through fluorescence intensities, and the relationship is specified based on the kinetics of the reaction. The model incorporates the intrinsic random nature of the process as well as measurement error. Taking a Bayesian inferential approach, marginal posterior distributions of the quantities of interest are estimated using Markov chain Monte Carlo. The method is applied to simulated data and an experimental data set.
我们提出了一种用于定量实时聚合酶链反应（PCR）数据的贝叶斯层次模型，旨在对不同生物样本中的DNA拷贝数进行相对定量。该模型是根据在聚合酶链反应的连续循环中测量的荧光强度的隐马尔可夫模型来指定的。假设反应效率通过荧光强度取决于目标DNA的丰度，并且基于反应动力学来指定这种关系。该模型纳入了过程的内在随机性质以及测量误差。采用贝叶斯推断方法，使用马尔可夫链蒙特卡罗估计感兴趣量的边际后验分布。该方法应用于模拟数据和一个实验数据集。
