Analysis of Differential Gene Expression Studies

  • D. Scholtens
  • A. von Heydebreck
Part of the Statistics for Biology and Health book series (SBH)


In this chapter, we focus on the analysis of differential gene expression studies. Many microarray studies are designed to detect genes associated with different phenotypes, for example, the comparison of cancer tumors and normal cells. In some multifactor experiments, genetic networks are perturbed with various treatments to understand the effects of those treatments and their interactions with each other in the dynamic cellular network. For even the simplest experiments, investigators must consider several issues for appropriate gene selection. We discuss strategies for geneat-a-time analyses, nonspecific and meta-data driven prefiltering techniques, and commonly used test statistics for detecting differential expression. We show how these strategies and statistical tools are implemented and used in Bioconductor. We also demonstrate the use of factorial models for probing complex biological systems and highlight the importance of carefully coordinating known cellular behavior with statistical modeling to make biologically relevant inference from microarray studies.


Gene Ontology Outlier Detection Limma Package Single Outlier Multiple Testing Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • D. Scholtens
  • A. von Heydebreck

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