Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data
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* Corresponding author: Kerby Shedden kshedden@umich.edu
BMC Bioinformatics 2005, 6:26 doi:10.1186/1471-2105-6-26
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Methods for evaluating gene expression from Affymetrix microarray datasets Ning Jiang, Lindsey J Leach, Xiaohua Hu, Elena Potokina, Tianye Jia, Arnis Druka, Robbie Waugh, Michael J Kearsey, Zewei W Luo BMC Bioinformatics 2008, 9:284 (17 June 2008) Comparison of the most commonly used statistical methods for assessing differential gene expression from Affymetrix microarrays on a high-quality dataset shows that PDNN is the best performer, clearly superior to the standard Affymetrix MAS5.0 method.
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Jun Ma, Zhaohui S Qin BMC Proceedings 2007, 1(Suppl 1):S154 (18 December 2007) |
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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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