Normalizing bead-based microRNA expression data: a measurement error model-based approach
Normalizing bead-based microRNA expression data: a measurement error model-based approach
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DOI：
10.1093/bioinformatics/btr180
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发表时间：
2011-06-01
期刊：
影响因子：
5.8
通讯作者：
Xi, Yaguang
中科院分区：
文献类型：
--
作者：
Wang, Bin;Wang, Xiao-Feng;Xi, Yaguang
动机：与互补DNA(CDNA)或信使RNA(MRNA)微阵列数据相比，microRNA(MiRNA)微阵列数据更难标准化，因为miRNAs的总数很少，而且大多数miRNAs的表达水平通常较低。在基于珠粒的微阵列中，杂交在几个池中完成。因此，在每个池中测试的miRNAs的数量甚至更少，这给样本内归一化带来了额外的困难，并最终影响到由不同池组装的最终轮廓的质量。本文研究了一种基于测量误差模型的珠基微阵列样本内归一化方法。结果：在本研究中，定量实时聚合酶链式反应(qRT-PCR)检测结果被用作验证的金标准。通过仿真研究和真实的基于微珠的miRNA表达数据，对所提出的基于测量误差模型的方法进行了性能评估。仿真结果表明，该方法能很好地从不同油气藏的子剖面中拼接出完整的剖面。与制造商推荐的两种样本内归一化方法相比，该方法产生了更健壮的最终完整图谱，并且在识别差异表达的miRNAs方面与qRT-PCR结果更一致，从而提高了两个微阵列平台之间的重复性。提出的样本内归一化方法和分位数归一化作为后续的互补性样本间归一化方法得到了有意义的结果。
Motivation: Compared with complementary DNA ( cDNA) or messenger RNA ( mRNA) microarray data, microRNA ( miRNA) microarray data are harder to normalize due to the facts that the total number of miRNAs is small, and that the majority of miRNAs usually have low expression levels. In bead-based microarrays, the hybridization is completed in several pools. As a result, the number of miRNAs tested in each pool is even smaller, which poses extra difficulty to intrasample normalization and ultimately affects the quality of the final profiles assembled from various pools. In this article, we consider a measurement error model-based method for bead-based microarray intrasample normalization.Results: In this study, results from quantitative real-time PCR (qRT-PCR) assays are used as 'gold standards' for validation. The performance of the proposed measurement error model-based method is evaluated via a simulation study and real bead-based miRNA expression data. Simulation results show that the new method performs well to assemble complete profiles from subprofiles from various pools. Compared with two intrasample normalization methods recommended by the manufacturer, the proposed approach produces more robust final complete profiles and results in better agreement with the qRT-PCR results in identifying differentially expressed miRNAs, and hence improves the reproducibility between the two microarray platforms. Meaningful results are obtained by the proposed intrasample normalization method, together with quantile normalization as a subsequent complemental intersample normalization method.