title: The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis.
abstract: BACKGROUND
  Quantile and rank normalizations are two widely used pre-processing techniques designed to remove technological noise presented in genomic data. Subsequent statistical analysis such as gene differential expression analysis is usually based on normalized expressions. In this study, we find that these normalization procedures can have a profound impact on differential expression analysis, especially in terms of testing power.
  
  
  RESULTS
  We conduct theoretical derivations to show that the testing power of differential expression analysis based on quantile or rank normalized gene expressions can never reach 100% with fixed sample size no matter how strong the gene differentiation effects are. We perform extensive simulation analyses and find the results corroborate theoretical predictions.
  
  
  CONCLUSIONS
  Our finding may explain why genes with well documented strong differentiation are not always detected in microarray analysis. It provides new insights in microarray experimental design and will help practitioners in selecting proper normalization procedures.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA.
authors.forename: Xing
authors.initials: X
authors.lastname: Qiu
authors.forename: Hulin
authors.initials: H
authors.lastname: Wu
authors.forename: Rui
authors.initials: R
authors.lastname: Hu
mesh_terms: D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size