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Weighted Analysis of Microarray Experiments

Doctoral thesis
Authors Anders Sjögren
Date of public defense 2007-05-24
Opponent at public defense doktor Anne-Mette Hein, Molecular Diagnostic Laboratory, Aarhus University Hospital, Denmark
ISBN 978-91-7291-939-6
Publisher Chalmers University of Technology
Place of publication Göteborg
Publication year 2007
Published at Department of Mathematical Sciences, Mathematical Statistics
Language en
Keywords DNA microarray, differential expression, gene expression, quality control (QC), quality assurance (QA), quality assessment, generalised linear model, empirical Bayes, weighted moderated statistic, invalid p-value, heteroscedasticity, WAME
Subject categories Mathematical statistics, Bioinformatics and Systems Biology, Genetic engineering including functional genomics, Internal medicine


DNA microarrays are strikingly efficient tools for analysing gene expression for large sets of genes simultaneously. The aim is often to identify genes which are differentially expressed between some studied conditions, thereby gaining insight into which cellular mechanisms are differently active between the conditions. In the measurement process, several steps exist that risk going partly or entirely wrong and quality control is therefore crucial.

In Paper I-III, a novel method is developed which integrates quality control quantitatively into the analysis of microarray experiments. The noise structure for each gene is modelled by (i) a global covariance structure matrix catching decreased quality by array-wise variances and catching shared sources of variation by correlations, and (ii) gene-wise variance scales having a prior distribution with parameters estimated from the data of all genes in an empirical Bayes manner. The variances and correlations are entirely estimated from the data. In the estimates and tests for differential expression, arrays with lower precision or arrays sharing sources of variation are downweighted. Thus, the sharp decision of entirely excluding arrays is avoided. The method is called Weighted Analysis of Microarray Experiments (WAME).

Current methods for microarray analysis generally disregard the quality variations. Simulations based on real data show that this often results in severely invalid p-values. Trusting such p-values therefore risks resulting in false biological conclusions. WAME gives increased power and valid p-values when few genes are differentially expressed and conservative p-values otherwise. Similar results are seen on simulations according to the model.

In Paper IV, WAME is used to identify genes which are differentially expressed between small and large human fat cells. WAME here successfully downweights one array that was suspected of decreased quality on biological grounds.

The WAME method is freely available as a add-on package for the R language.

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