Statistical methods for ranking differentially expressed genes
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Correspondence: Per Broberg per.broberg@astrazeneca.com
Genome Biology 2003, 4:R41 doi:10.1186/gb-2003-4-6-r41
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BioMed Central: 6 citations
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Methodology article
Semi-supervised discovery of differential genes Shigeyuki Oba, Shin lshii BMC Bioinformatics 2006, 7:414 (18 September 2006) |
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Research
Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution Jaroslav P Novak, Seon-Young Kim, Jun Xu, Olga Modlich, David J Volsky, David Honys, Joan L Slonczewski, Douglas A Bell, Fred R Blattner, Eduardo Blumwald, Marjan Boerma, Manuel Cosio, Zoran Gatalica, Marian Hajduch, Juan Hidalgo, Roderick R McInnes, Merrill C Miller III, Milena Penkowa, Michael S Rolph, Jordan Sottosanto, Rene St-Arnaud, Michael J Szego, David Twell, Charles Wang Biology Direct 2006, 1:27 (7 September 2006) |
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Methodology article
A comparative review of estimates of the proportion unchanged genes and the false discovery rate Per Broberg BMC Bioinformatics 2005, 6:199 (8 August 2005) |
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Research article
Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models Pingzhao Hu, Celia MT Greenwood, Joseph Beyene BMC Bioinformatics 2005, 6:128 (27 May 2005) |
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Research
Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset Sung E Choe, Michael Boutros, Alan M Michelson, George M Church, Marc S Halfon Genome Biology 2005, 6:R16 (28 January 2005) A 'spike-in' experiment for Affymetrix GeneChips is described that provides a defined dataset of 3,860 RNA species. A 'best route' combination of analysis methods is presented which allows detection of approximately 70% of true positives before reaching a 10% false discovery rate. |
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Methodology article
Jason Comander, Sripriya Natarajan, Michael A Gimbrone, Guillermo García-Cardeña BMC Genomics 2004, 5:17 (27 February 2004) |