No control genes required: Bayesian analysis of qRT-PCR data.
No control genes required: Bayesian analysis of qRT-PCR data.
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DOI：
10.1371/journal.pone.0071448
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Scott JG
中科院分区：
文献类型：
--
作者：
Matz MV;Wright RM;Scott JG
定量逆转录PCR（qRT-PCR）数据的基于模型的分析可能比传统方法更强大和通用。然而，现有的基于模型的方法不能正确地处理与低丰度目标相关的较高的采样方差，也没有提供一种自然的方式将控制基因的稳定性的假设直接纳入模型拟合过程。在我们的方法中，原始qPCR数据表示为分子计数，并在泊松对数正态误差下使用广义线性混合模型进行描述。一个马尔可夫链蒙特卡罗（MCMC）算法被用来从联合后验分布的样本在所有的模型参数，从而估计所有的实验因素对每个基因的表达的影响。基于泊松的模型允许PCR扩增过程的均值-方差关系的正确规范，并且还可以从无扩增（零计数）的实例中收集信息。我们的方法是非常灵活的控制基因：任何先验知识的预期程度，其稳定性可以直接纳入模型。然而，该方法在没有这些假设的情况下，甚至在完全没有控制基因的情况下，也提供了合理的答案。我们还提出了一个自然贝叶斯模拟的“经典”分析，它使用标准的数据预处理步骤（对数转换和多基因归一化），但估计所有的基因表达变化联合在一个单一的模型。新方法比基于成对t检验的标准Δ-Δ Ct分析更加灵活和强大。我们的方法将相对定量分析方案的适用性一直扩展到最低丰度的目标，并提供了一个新的机会来分析qRT-PCR数据，而无需对目标稳定性进行任何假设。这些过程已经在R中作为MCMC.qpcr包实现。
Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.
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