Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls
Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls
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DOI：
10.1016/s1672-0229(06)60021-1
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发表时间：
2006-01-01
影响因子：
9.5
通讯作者：
Benecke, Arndt
中科院分区：
文献类型：
--
作者：
Noth, Sebastian;Brysbaert, Guillaume;Benecke, Arndt
利用微阵列技术对高通量全局基因表达的研究产生了越来越大量的系统性转录组数据。利用这些异质数据集的一个主要挑战是如何通过分析间方法将表达谱归一化。已经开发了不同的非线性和线性归一化方法，其基本上依赖于两种不同测定之间的真实或感知对数倍数变化分布在性质上是对称的这一假设。然而，经常观察到不对称的基因表达变化，导致次优的标准化结果，并因此可能导致数千个错误的调用。因此，我们专门研究了不对称比较转录组谱，并使用加权负二阶指数误差函数（NeONORM）开发了标准化，以实现稳健和全局的试验间标准化。NeONORM有效地抑制了真实的基因调控事件，以最大限度地减少它们对正常化过程的误导性影响。我们使用人工和真实的实验数据集评估了NeONORM的适用性，这两个数据集都表明NeONORM可以系统地应用于测定间和条件间的比较。
Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions (NeONORM) for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the normalization process. We evaluated NeONORM's applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.