2006
DOI: 10.1016/s1672-0229(06)60021-1
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Normalization Using Weighted Negative Second Order Exponential Error Functions (NeONORM) Provides Robustness Against Asymmetries in Comparative Transcriptome Profiles and Avoids False Calls

Abstract: 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. Howe… Show more

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Cited by 29 publications
(28 citation statements)
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References 34 publications
(38 reference statements)
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“…Statistical analysis of the microarray data was carried out as described previously (Noth and Benecke 2005;Noth et al 2006;Van Maele et al 2010).…”
Section: Discussionmentioning
confidence: 99%
“…Statistical analysis of the microarray data was carried out as described previously (Noth and Benecke 2005;Noth et al 2006;Van Maele et al 2010).…”
Section: Discussionmentioning
confidence: 99%
“…We previously described acute data analysis of ABI arrays (26,49). Briefly, normalization was achieved using the NeONORM method (50). Significance of log2 (fold change) (designated "log2Q") was determined based on a mixture lognormal distribution hypothesis of signal intensities using mixture ANOVA methodology (51).…”
Section: Methodsmentioning
confidence: 99%
“…Data quality was determined using a QC procedure (41). Data were normalized using NeONORM with k = 0.02 (42)(43)(44). Subtraction profiling was performed as in refs.…”
Section: Methodsmentioning
confidence: 99%