2002
DOI: 10.1016/s0167-9473(01)00046-9
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A mixture model approach for the analysis of microarray gene expression data

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Cited by 309 publications
(284 citation statements)
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“…A t test for genotype differences was conducted as part of a mixed linear model analysis for each gene (Wolfinger et al, 2001) yielding 10,767 P-values. As described by Allison et al (2002), a mixture of uniform and a b distribution was fit to the observed distribution of the 10,767 P-values obtained from the mixed linear model analysis. The estimated parameters from the fit of the mixture model were used to estimate the posterior probability of differential expression for each gene and to estimate the proportion of false positive results among all genes with P-values #0.01 and estimated fold change .…”
Section: Discussionmentioning
confidence: 99%
“…A t test for genotype differences was conducted as part of a mixed linear model analysis for each gene (Wolfinger et al, 2001) yielding 10,767 P-values. As described by Allison et al (2002), a mixture of uniform and a b distribution was fit to the observed distribution of the 10,767 P-values obtained from the mixed linear model analysis. The estimated parameters from the fit of the mixture model were used to estimate the posterior probability of differential expression for each gene and to estimate the proportion of false positive results among all genes with P-values #0.01 and estimated fold change .…”
Section: Discussionmentioning
confidence: 99%
“…The pp is the Bayesian probability that a gene with a frequentist P value less than or equal to that observed is a gene for which there is a real difference between groups in expression level. This procedure provides an omnibus test of whether there is any overall effect of treatment on gene expression levels, an estimate of the total number of genes that have their expression levels altered, and a model describing the distribution of effects on gene expression levels (27).…”
Section: Methodsmentioning
confidence: 99%
“…The Bayesian posterior probability of being a false discovery (expressed as false discovery rate [FDR]) was estimated for each probe set individually, based on the Welch p values, using a mixture model described elsewhere [23,24]. We focused on the genes among those most differentially expressed that had corresponding probe sets with a less than 0.1% FDR, i.e.…”
Section: Data Analysesmentioning
confidence: 99%