The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis.

BMC Bioinformatics (Impact Factor: 2.67). 04/2013; 14(1):124. DOI: 10.1186/1471-2105-14-124
Source: PubMed

ABSTRACT Background Quantile and rank normalizations are two widely used pre-processingtechniques designed to remove technological noise presented ingenomic data. Subsequent statistical analysis such as genedifferential expression analysis is usually based on normalizedexpressions. In this study, we find that these normalizationprocedures can have a profound impact on differential expressionanalysis, especially in terms of testing power.Results We conduct theoretical derivations to show that the testing power ofdifferential expression analysis based on quantile or ranknormalized gene expressions can never reach 100% with fixed samplesize no matter how strong the gene differentiation effects are.We perform extensive simulation analyses and find theresults corroborate theoretical predictions.Conclusions Our finding may explain why genes with well documentedstrong differentiation are not always detected in microarrayanalysis. It provides new insights in microarray experimental design and will helppractitioners in selecting proper normalization procedures.

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