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Normalization Using Weighted Negative Second Order Exponential Error Functions(NeONORM) Provides Robustness Against Asym-metries in Comparative Transcriptome Profiles and Avoids False Calls

查看全文 作　　者：Sebastian [1]Noth;Guillaume [1]Brysbaert;Arndt [1]Benecke 高影响力作者 机构地区：[1]Systems Epigenomics Group, Institut des Hautes Etudes Scientifiques/Institut de Recherches Interdisciplinaires,CNRS/INSERM, 91440 Bures sur Yvette, France.高影响力机构 出　　处：《Genomics, Proteomics & Bioinformatics》索引2006年第4卷第2期,共20页高影响力期刊 摘　　要：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 expres- sion 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 fre- quently 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 nor- malization 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. 关 键 词：指数误差 统计分析 对称性 生物学
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