Cross-study validation and combined analysis of gene expression microarray data

Elizabeth Garrett-Mayer, Giovanni Parmigiani, Xiaogang Zhong, Leslie Cope, Edward Gabrielson

Research output: Contribution to journalArticlepeer-review

Abstract

Investigations of transcript levels on a genomic scale using hybridization-based arrays have led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy because of the probabilistic nature of the conclusions and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article, we present simple and effective data analysis and visualization tools for gauging the degree to which the findings of one study are reproduced by others and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer and illustrate that it is possible to identify a substantial biologically relevant subset of the human genome within which hybridization results are reliable. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, laboratories and populations. Important biological signals are often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking and can become a routine component of genomic analysis.

Original languageEnglish (US)
Pages (from-to)333-354
Number of pages22
JournalBiostatistics
Volume9
Issue number2
DOIs
StatePublished - Apr 2008

Keywords

  • Breast cancer
  • Intraclass correlation
  • Meta-analysis
  • Prinicipal components
  • Reliability

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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