Statistical analysis of real-time PCR data
Statistical analysis of real-time PCR data
复制标题
DOI：
10.1186/1471-2105-7-85
复制
发表时间：
2006-02-22
影响因子：
3
通讯作者：
Stewart, CN
中科院分区：
文献类型：
--
作者：
Yuan, JS;Reed, A;Stewart, CN
背景资料：尽管实时PCR已广泛应用于生物医学科学，但仍然缺乏用于定量实时PCR分析的数据处理程序;特别是在适当的统计处理领域。置信区间和统计显著性考虑在当前的许多数据分析方法中并不明确。基于标准曲线法和其他有用的数据分析方法，我们提出并比较了四种统计方法和模型的实时PCR data.Results分析：在第一种方法中，多元回归分析模型被开发出来的基因和治疗效果的相互作用的估计得到Δ Δ Ct。在第二种方法中，提出了一个ANCOVA（协方差分析）模型，Delta Delta Ct可以从变量的影响分析中得出。其他两个模型涉及计算Delta Ct，然后进行两组t检验和非参数类似Wilcoxon检验。SAS程序开发的所有四个模型和数据输出的样本集的分析。此外，数据质量控制模型的开发和实施，使用SAS.Conclusion：实用的统计解决方案与SAS程序开发的实时PCR数据和样本数据集进行了分析与SAS程序。使用各种模型和程序进行的分析得出了类似的结果。这里提出的数据质量控制和分析程序提供了使用实时PCR估计基因相对表达的统计要素。
Background: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.Results: In the first approach, a multiple regression analysis model was developed to derive Delta Delta Ct from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA ( analysis of covariance) model was proposed, and the Delta Delta Ct can be derived from analysis of effects of variables. The other two models involve calculation Delta Ct followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS.Conclusion: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.