title: Effect of various normalization methods on Applied Biosystems expression array system data.
abstract: BACKGROUND
  DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays.
  
  
  RESULTS
  We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists.
  
  
  CONCLUSIONS
  Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System.
authors.affiliation: Molecular Biology Division, Applied Biosystems, Foster City, CA 94404, USA. catalin.barbacioru@appliedbiosystems.com
authors.forename: Catalin C
authors.initials: CC
authors.lastname: Barbacioru
authors.forename: Yulei
authors.initials: Y
authors.lastname: Wang
authors.forename: Roger D
authors.initials: RD
authors.lastname: Canales
authors.forename: Yongming A
authors.initials: YA
authors.lastname: Sun
authors.forename: David N
authors.initials: DN
authors.lastname: Keys
authors.forename: Frances
authors.initials: F
authors.lastname: Chan
authors.forename: Karen A
authors.initials: KA
authors.lastname: Poulter
authors.forename: Raymond R
authors.initials: RR
authors.lastname: Samaha
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity