Effect of various normalization methods on Applied Biosystems expression array system data

被引:31
作者
Barbacioru, Catalin C. [1 ]
Wang, Yulei [1 ]
Canales, Roger D. [1 ]
Sun, Yongming A. [1 ]
Keys, David N. [1 ]
Chan, Frances [1 ]
Poulter, Karen A. [1 ]
Samaha, Raymond R. [1 ]
机构
[1] Appl Biosyst Inc, Div Mol Biol, Foster City, CA 94404 USA
关键词
D O I
10.1186/1471-2105-7-533
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 (R) 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. Conclusion: 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 (R) 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.
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页数:14
相关论文
共 20 条
[1]   CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].
BENJAMINI, Y ;
HOCHBERG, Y .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300
[2]   A comparison of normalization methods for high density oligonucleotide array data based on variance and bias [J].
Bolstad, BM ;
Irizarry, RA ;
Åstrand, M ;
Speed, TP .
BIOINFORMATICS, 2003, 19 (02) :185-193
[3]   Evaluation of DNA microarray results with quantitative gene expression platforms [J].
Canales, Roger D. ;
Luo, Yuling ;
Willey, James C. ;
Austermiller, Bradley ;
Barbacioru, Catalin C. ;
Boysen, Cecilie ;
Hunkapiller, Kathryn ;
Jensen, Roderick V. ;
Knight, Charles R. ;
Lee, Kathleen Y. ;
Ma, Yunqing ;
Maqsodi, Botoul ;
Papallo, Adam ;
Peters, Elizabeth Herness ;
Poulter, Karen ;
Ruppel, Patricia L. ;
Samaha, Raymond R. ;
Shi, Leming ;
Yang, Wen ;
Zhang, Lu ;
Goodsaid, Federico M. .
NATURE BIOTECHNOLOGY, 2006, 24 (09) :1115-1122
[4]   LOCALLY WEIGHTED REGRESSION - AN APPROACH TO REGRESSION-ANALYSIS BY LOCAL FITTING [J].
CLEVELAND, WS ;
DEVLIN, SJ .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1988, 83 (403) :596-610
[5]  
Dudoit S, 2002, STAT SINICA, V12, P111
[6]   A novel method for real time quantitative RT PCR [J].
Gibson, UEM ;
Heid, CA ;
Williams, PM .
GENOME RESEARCH, 1996, 6 (10) :995-1001
[7]   Rat toxicogenomic study reveals analytical consistency across microarray platforms [J].
Guo, Lei ;
Lobenhofer, Edward K. ;
Wang, Charles ;
Shippy, Richard ;
Harris, Stephen C. ;
Zhang, Lu ;
Mei, Nan ;
Chen, Tao ;
Herman, Damir ;
Goodsaid, Federico M. ;
Hurban, Patrick ;
Phillips, Kenneth L. ;
Xu, Jun ;
Deng, Xutao ;
Sun, Yongming Andrew ;
Tong, Weida ;
Dragan, Yvonne P. ;
Shi, Leming .
NATURE BIOTECHNOLOGY, 2006, 24 (09) :1162-1169
[8]   Microarray data - the USFDA, industry and academia [J].
Hackett, JL ;
Lesko, LJ .
NATURE BIOTECHNOLOGY, 2003, 21 (07) :742-743
[9]   Maximum likelihood estimation of optimal scaling factors for expression array normalization [J].
Hartemink, AJ ;
Gifford, DK ;
Jaakkola, TS ;
Young, RA .
MICROARRAYS: OPTICAL TECHNOLOGIES AND INFORMATICS, 2001, 4266 :132-140
[10]   Real time quantitative PCR [J].
Heid, CA ;
Stevens, J ;
Livak, KJ ;
Williams, PM .
GENOME RESEARCH, 1996, 6 (10) :986-994