Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs--a comparative study.
Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs--a comparative study.
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DOI：
10.1371/journal.pone.0038946
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Pfaffl MW
中科院分区：
文献类型：
--
作者：
Meyer SU;Kaiser S;Wagner C;Thirion C;Pfaffl MW
适当的正常化可以最大限度地减少系统性技术变化的影响，是获得有意义的生物变化的先决条件。然而，关于miRNA标准化性能和建议存在不一致。因此，我们研究了七种不同的标准化方法（参考基因指数，全局几何平均值，分位数，不变选择，黄土，loessM和广义procrustes分析）对两种不同和常用的miRNA分析平台的平台内和平台间性能的影响。我们纳入了来自基于杂交的平台（Agilent Technologies）和RT-qPCR平台（Applied Biosystems）的miRNA谱分析数据。此外，我们通过单独的RT-qPCR测定验证了miRNA的子集。我们的分析纳入了分化和肿瘤坏死因子α治疗对原代人骨骼肌细胞和小鼠骨骼肌细胞系的影响的数据。不同的标准化方法对（i）标准差，（ii）受试者工作特征（ROC）曲线下面积，（iii）差异表达的相似性的影响不同。黄土、黄土M和分位数分析在Agilent和TLDA平台上最大限度地减少标准偏差方面最为有效。此外，黄土，loessM，不变选择和广义procrustes分析增加了ROC曲线下面积，这是测试统计性能的一个指标。Jaccard指数表明，黄土，黄土M，分位数，GPA标准化的AGL和TLDA数据以及RGI标准化的TLDA数据的差异表达的平台间一致性往往增加。我们建议将黄土或loessM和GPA标准化应用于miRNA Agilent阵列和qPCR卡，因为这些标准化方法表明（i）有效降低标准偏差，（ii）提高差异miRNA表达检测的灵敏度和准确性以及（iii）提高平台间一致性。结果表明，loessM和广义procrustes分析法成功应用于单色miRNA谱分析实验。
Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments.
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影响因子：
14.9
作者：
Chen C;Ridzon DA;Broomer AJ;Zhou Z;Lee DH;Nguyen JT;Barbisin M;Xu NL;Mahuvakar VR;Andersen MR;Lao KQ;Livak KJ;Guegler KJ
通讯作者：
Guegler KJ
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
2.7
作者：
Pradervand, Sylvain;Weber, Johann;Harshman, Keith
通讯作者：
Harshman, Keith
影响因子：
4.9
作者：
METZ, CE
通讯作者：
METZ, CE
影响因子：
4.8
作者：
Schmittgen, Thomas D.;Lee, Eun Joo;Chen, Caifu
通讯作者：
Chen, Caifu