Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models
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* Corresponding author: Joseph Beyene joseph@utstat.toronto.edu
BMC Bioinformatics 2005, 6:128 doi:10.1186/1471-2105-6-128
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BioMed Central: 8 citations
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Methodology article
Using the ratio of means as the effect size measure in combining results of microarray experiments Pingzhao Hu, Celia MT Greenwood, Joseph Beyene BMC Systems Biology 2009, 3:106 (5 November 2009) |
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Methodology article
Combining transcriptional datasets using the generalized singular value decomposition Andreas W Schreiber, Neil J Shirley, Rachel A Burton, Geoffrey B Fincher BMC Bioinformatics 2008, 9:335 (8 August 2008) |
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Methodology article
MAID : An effect size based model for microarray data integration across laboratories and platforms Ivan Borozan, Limin Chen, Bryan Paeper, Jenny E Heathcote, Aled M Edwards, Michael Katze, Zhaolei Zhang, Ian D McGilvray BMC Bioinformatics 2008, 9:305 (10 July 2008) |
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Research article
Xiangrong Kong, Valeria Mas, Kellie J Archer BMC Genomics 2008, 9:98 (26 February 2008) |
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Research article
Application of a correlation correction factor in a microarray cross-platform reproducibility study Kellie J Archer, Catherine I Dumur, G Scott Taylor, Michael D Chaplin, Anthony Guiseppi-Elie, Geraldine Grant, Andrea Ferreira-Gonzalez, Carleton T Garrett BMC Bioinformatics 2007, 8:447 (15 November 2007) |
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Methodology article
Bayesian meta-analysis models for microarray data: a comparative study Erin M Conlon, Joon J Song, Anna Liu BMC Bioinformatics 2007, 8:80 (7 March 2007) Of two recent ways to combine the results of microarrays using Bayesian statistics the simpler approach, which ignores variability across studies, actually performs better.
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Methodology article
Bayesian models for pooling microarray studies with multiple sources of replications Erin M Conlon, Joon J Song, Jun S Liu BMC Bioinformatics 2006, 7:247 (5 May 2006) A new Bayesian hierarchical method involving pooling the data of cDNA microarray experiments from multiple independent studies identifies more truly differentially expressed genes than single independent studies.
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Methodology article
Tests for differential gene expression using weights in oligonucleotide microarray experiments Pingzhao Hu, Joseph Beyene, Celia MT Greenwood BMC Genomics 2006, 7:33 (22 February 2006) |