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Enhanced Quantile Normalization of Microarray Data to Reduce Loss of Information in Gene Expression Profiles
Author(s) -
Hu Jianhua,
He Xuming
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00670.x
Subject(s) - normalization (sociology) , quantile , computer science , data mining , microarray analysis techniques , gene expression profiling , computational biology , gene expression , statistics , biology , gene , mathematics , genetics , sociology , anthropology
Summary In microarray experiments, removal of systematic variations resulting from array preparation or sample hybridization conditions is crucial to ensure sensible results from the ensuing data analysis. For example, quantile normalization is routinely used in the treatment of both oligonucleotide and cDNA microarray data, even though there might be some loss of information in the normalization process. We recognize that the ideal normalization, if it ever exists, would aim to keep the maximal amount of gene profile information with the lowest possible noise. With this objective in mind, we propose a valuable enhancement to quantile normalization, and demonstrate through three Affymetrix experiments that the enhanced normalization can result in better performance in detecting and ranking differentially expressed genes across experimental conditions.