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Analysis of whole-genome microarray replicates using mixed models.

Lorenz Wernisch1, Sharon L Kendall, Shamit Soneji

  • 1School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk

Bioinformatics (Oxford, England)
|December 25, 2002
PubMed
Summary
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Replication in microarray experiments is crucial for accurate gene expression analysis. This study introduces novel methods for noise reduction and differential expression detection in Mycobacterium tuberculosis data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Microbiology

Background:

  • Microarray experiments are prone to noise, necessitating replication for reliable fold-change estimation.
  • Understanding the dependency structure of replication is vital for accurate noise analysis.

Purpose of the Study:

  • To identify differentially expressed genes in Mycobacterium tuberculosis.
  • To develop new methods for filtering, normalizing, and imputing missing values in raw microarray data.
  • To determine optimal sample and array numbers for robust analysis.

Main Methods:

  • Mixed ANOVA models were employed to quantify sources of error.
  • Hierarchical bootstrapping on scaled residuals was used for significance testing of differential expression.
  • Analysis of replicate data sets from a Mycobacterium tuberculosis trcS mutant.

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Main Results:

  • Novel methods for data filtering, normalization, and imputation were proposed.
  • Mixed ANOVA models successfully quantified error sources and informed optimal experimental design.
  • A transcriptional readthrough artifact leading to apparent gene upregulation was identified.

Conclusions:

  • The developed methods enhance the accuracy of gene expression analysis in noisy microarray data.
  • Understanding error sources and replication structure is key to reliable differential gene expression findings.
  • The YASMA package in R provides accessible tools for these advanced analyses.