Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis
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* Corresponding author: Reinhard Hoffmann r_hoffmann@m3401.mpk.med.uni-muenchen.de
Genome Biology 2002, 3:research0033-research0033.11 doi:10.1186/gb-2002-3-7-research0033
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BioMed Central: 15 citations
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Research article
Sheila M Dreher-Lesnick, Shane M Ceraul, M Sayeedur Rahman, Abdu F Azad BMC Microbiology 2008, 8:61 (15 April 2008) |
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Methodology article
Thierry Rème, Dirk Hose, John De Vos, Aurélien Vassal, Pierre-Olivier Poulain, Véronique Pantesco, Hartmut Goldschmidt, Bernard Klein BMC Bioinformatics 2008, 9:16 (11 January 2008) |
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Proceedings
Jun Ma, Zhaohui S Qin BMC Proceedings 2007, 1(Suppl 1):S154 (18 December 2007) |
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Method
Model-based analysis of two-color arrays (MA2C) Jun S Song, W Evan Johnson, Xiaopeng Zhu, Xinmin Zhang, Wei Li, Arjun K Manrai, Jun S Liu, Runsheng Chen, X Shirley Liu Genome Biology 2007, 8:R178 (29 August 2007) A normalization method based on probe GC content for two-color tiling arrays and an algorithm for detecting peak regions are presented. They are available in a stand-alone Java program. |
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Research article
Normalization and experimental design for ChIP-chip data Shouyong Peng, Artyom A Alekseyenko, Erica Larschan, Mitzi I Kuroda, Peter J Park BMC Bioinformatics 2007, 8:219 (25 June 2007) |
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Research article
Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips Andrew P Harrison, Caroline E Johnston, Christine A Orengo BMC Bioinformatics 2007, 8:195 (11 June 2007) |
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Method
Towards the uniform distribution of null P values on Affymetrix microarrays Anthony A Fodor, Timothy L Tickle, Christine Richardson Genome Biology 2007, 8:R69 (1 May 2007) Estimating the |
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Method
ProCAT: a data analysis approach for protein microarrays Xiaowei Zhu, Mark Gerstein, Michael Snyder Genome Biology 2006, 7:R110 (16 November 2006) ProCAT, a powerful and flexible new approach for analyzing many types of protein microarrays, is described. |
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Research article
Roel GW Verhaak, Frank JT Staal, Peter JM Valk, Bob Lowenberg, Marcel JT Reinders, Dick de Ridder BMC Bioinformatics 2006, 7:105 (2 March 2006) Preprocessing normalization methods can strongly affect the results of a small microarray study; low-level analysis focusing on expression levels of specific transcripts is affected more profoundly than high-level multivariate analysis.
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Research article
Kerby Shedden, Wei Chen, Rork Kuick, Debashis Ghosh, James Macdonald, Kathleen R Cho, Thomas J Giordano, Stephen B Gruber, Eric R Fearon, Jeremy MG Taylor, Samir Hanash BMC Bioinformatics 2005, 6:26 (10 February 2005) |
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Research article
Classification between normal and tumor tissues based on the pair-wise gene expression ratio YeeLeng Yap, XueWu Zhang, MT Ling, XiangHong Wang, YC Wong, Antoine Danchin BMC Cancer 2004, 4:72 (7 October 2004) |
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Method
Model selection and efficiency testing for normalization of cDNA microarray data Matthias Futschik, Toni Crompton Genome Biology 2004, 5:R60 (30 July 2004) This study presents two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. |
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Methodology article
Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays Chao Lu BMC Bioinformatics 2004, 5:103 (29 July 2004) |
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Research
Julien Sylvestre, Stéphane Vialette, Marisol Corral Debrinski, Claude Jacq Genome Biology 2003, 4:R44 (6 June 2003) Mitochondrial biogenesis requires concerted expression of the many genes whose products make up the organelle. Combining biochemical fractionations with oligonucleotide array analyses allowed identification of interesting genes whose mRNA localization might be essential for mitochondrial biogenesis in most eukaryotic cells. |
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Research
Improved analytical methods for microarray-based genome-composition analysis Charles C Kim, Elizabeth A Joyce, Kaman Chan, Stanley Falkow Genome Biology 2002, 3:research0065-research0065.17 (29 October 2002) Genome-composition analysis using microarrays can be used to categorize genes into 'present' and 'divergent' categories. This involves selecting a signal value that is used as a cutoff to discriminate present and divergent genes, but this can result in the misclassification of many genes. A method is described that depends on the shape of the signal-ratio distribution and does not require empirical determination of a cutoff. Many genes previously classified as present using static methods are in fact divergent on the basis of microarray signal; this is corrected by our algorithm. |