Selection of optimal reference genes for normalization in quantitative RT-PCR.
Selection of optimal reference genes for normalization in quantitative RT-PCR.
复制标题
DOI：
10.1186/1471-2105-11-253
复制
发表时间：
2010-05-14
影响因子：
3
通讯作者：
Hyslop T
中科院分区：
文献类型：
--
作者：
Chervoneva I;Li Y;Schulz S;Croker S;Wilson C;Waldman SA;Hyslop T
实时定量逆转录聚合酶链反应（qRT - PCR）中的归一化对于补偿实验变异是必要的。一种常用的归一化策略是使用参照基因，由于内在变异（在组织、个体等之间），这可能会给归一化表达水平引入额外的变异性。为了使这种内在变异性最小化，会使用多个参照基因。当前选择参照基因的方法假定它们的内在变异是相互独立的。这种假定并不总是合理的，可能会导致选择一组次优的参照基因。
我们提出了一种稳健的方法来选择参照基因的最优子集，使得相应归一化因子的方差最小。归一化因子方差的估计是基于所有可用候选参照基因的估计无结构协方差矩阵，并对所有可能的相关性进行调整。通过对所有候选参照基因数据进行自助抽样（bootstrapping），并获得对数转换后的归一化因子方差的自助法上置信限来实现稳健性。参照基因子集的选择根据以下标准之一进行优化：（A）使归一化因子的变异性最小；（B）在归一化因子变异性的可接受上限内使参照基因的数量最小；（C）使归一化因子方差的平均排名最小。与先前的工作不同，我们提出的方法评估各种大小的所有基因子集，而不是根据单个参照基因的稳定性对其进行排名。在两个公开可用的数据集和一个新的数据集中，我们的方法所确定的参照基因子集的归一化因子的经验方差比使用先前发表的方法所确定的子集更小。一项小型模拟研究表明，在候选参照基因之间存在即使是适度的、特别是负相关的情况下，我们提出的方法在识别真正最优参照子集的敏感性方面具有优势。
我们提出的方法基于候选参照基因的所有可能子集对归一化因子的变异性进行全面且稳健的评估。这种评估的结果为从选择参照基因最优子集的重要标准中进行选择提供了灵活性，除非一个子集满足所有标准。与当前的标准方法相比，这种方法能识别出归一化因子变异性更小的基因子集，特别是当候选基因之间存在一些非平凡的内在相关性时。
Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes. We propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes. The proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.
登录
查看更多内容
影响因子：
11.5
作者：
Schulz, Stephanie;Hyslop, Terry;Waldman, Scott A.
通讯作者：
Waldman, Scott A.
影响因子：
2.7
作者：
Aerts, JL;Gonzales, MI;Topalian, SL
通讯作者：
Topalian, SL
影响因子：
2.7
作者：
Dheda, K;Huggett, JF;Zumla, A
通讯作者：
Zumla, A
影响因子：
2.7
作者：
Abruzzo, LV;Lee, KY;Coombes, KR
通讯作者：
Coombes, KR
DOI：
10.1073/pnas.93.25.14827
发表时间：
1996-12-10
影响因子：
11.1
作者：
Carrithers, SL;Barber, MT;Waldman, SA
通讯作者：
Waldman, SA