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PubMed · 15882138

Bayesian normalization and identification for differential gene expression data.

Dabao Zhang·Martin T Wells·Christine D Smart·William E Fry
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Abstract

Commonly accepted intensity-dependent normalization in spotted microarray studies takes account of measurement errors in the differential expression ratio but ignores measurement errors in the total intensity, although the definitions imply the same measurement error components are involved in both statistics. Furthermore, identification of differentially expressed genes is usually considered separately following normalization, which is statistically problematic. By incorporating the measurement errors in both total intensities and differential expression ratios, we propose a measurement-error model for intensity-dependent normalization and identification of differentially expressed genes. This model is also flexible enough to incorporate intra-array and inter-array effects. A Bayesian framework is proposed for the analysis of the proposed measurement-error model to avoid the potential risk of using the common two-step procedure. We also propose a Bayesian identification of differentially expressed genes to control the false discovery rate instead of the ad hoc thresholding of the posterior odds ratio. The simulation study and an application to real microarray data demonstrate promising results.

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Bayes TheoremComputational BiologyComputer SimulationGene Expression ProfilingMarkov ChainsMonte Carlo MethodJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Review

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Dabao Zhang, Martin T Wells, Christine D Smart, William E Fry. 2005. Bayesian normalization and identification for differential gene expression data.. https://doi.org/10.1089/cmb.2005.12.391

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