Document Detail


Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls.
MedLine Citation:
PMID:  16970549     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Sebastian Noth; Guillaume Brysbaert; Arndt Benecke
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Genomics, proteomics & bioinformatics     Volume:  4     ISSN:  1672-0229     ISO Abbreviation:  Genomics Proteomics Bioinformatics     Publication Date:  2006 May 
Date Detail:
Created Date:  2006-09-14     Completed Date:  2006-10-25     Revised Date:  2012-09-10    
Medline Journal Info:
Nlm Unique ID:  101197608     Medline TA:  Genomics Proteomics Bioinformatics     Country:  China    
Other Details:
Languages:  eng     Pagination:  90-109     Citation Subset:  IM    
Affiliation:
Systems Epigenomics Group, Institut des Hautes Etudes Scientifiques/Institut de Recherches Interdisciplinaires, CNRS/INSERM, 91440 Bures sur Yvette, France.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Cell Line, Tumor
Data Interpretation, Statistical
Gene Expression Profiling* / methods
Gene Expression Regulation, Neoplastic*
Humans
Models, Genetic*
Oligonucleotide Array Sequence Analysis* / methods
Pattern Recognition, Automated / methods

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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