Weighted Analysis of Microarray Experiments
Doktorsavhandling, 2007

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.

heteroscedasticity

quality assurance (QA)

gene expression

quality control (QC)

differential expression

weighted moderated statistic

quality assessment

DNA microarray

generalised linear model

empirical Bayes

WAME

invalid p-value

sal Euler, Chalmers Tvärgata 3, Matematiska vetenskaper, Chalmers tekniska högskola
Opponent: doktor Anne-Mette Hein, Molecular Diagnostic Laboratory, Aarhus University Hospital, Denmark

Författare

Anders Sjögren

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Quality Optimised Analysis of General Paired Microarray Experiments

Statistical Applications in Genetics and Molecular Biology,; Vol. 5(2006)p. i-29

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Separation of human adipocytes by size: hypertrophic fat cells display distinct gene expression

FASEB Journal,; Vol. 20(2006)p. E832-E839

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Weighted Analysis of Paired Microarray Experiments

Statistical Applications in Genetics and Molecular Biology,; Vol. 4(2005)

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Weighted Analysis of General Microarray Experiments

BMC Bioinformatics,; Vol. 8(2007)p. 15-

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Ämneskategorier

Klinisk medicin

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

Annan industriell bioteknik

Fundament

Grundläggande vetenskaper

Styrkeområden

Livsvetenskaper och teknik

ISBN

978-91-7291-939-6

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 2620

sal Euler, Chalmers Tvärgata 3, Matematiska vetenskaper, Chalmers tekniska högskola

Opponent: doktor Anne-Mette Hein, Molecular Diagnostic Laboratory, Aarhus University Hospital, Denmark

Mer information

Skapat

2017-10-08