Regression Approaches for Microarray Data Analysis

Mark R. Segal, Kam D Dahlquist, Bruce R Conklin

Research output: Contribution to journalArticlepeer-review

Abstract

A variety of new procedures have been devised to handle the two sample comparison (e.g., tumor versus normal tissue) of gene expression values as measured with microarrays. Such new methods are required in part because of some defining characteristics of microarray-based studies: (i) the very large number of genes contributing expression measures which far exceeds the number of samples (observations) available, and (ii) the fact that by virtue of pathway/network relationships, the gene expression measures tend to be highly correlated. These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. Correspondingly, several methods have been recently proposed for addressing these issues. We briefly critique some of these methods prior to a detailed evaluation of gene harvesting. This reveals that gene harvesting, without additional constraints, can yield artifactual solutions. Results obtained employing such constraints motivate the use of regularized regression procedures such as the lasso, least angle regression, and support vector machines. Model selection and solution multiplicity issues are also discussed. The methods are evaluated using a microarraybased study of cardiomyopathy in transgenic mice.

Original languageEnglish
Pages (from-to)961-980
Number of pages20
JournalJournal of Computational Biology
Volume10
Issue number6
DOIs
StatePublished - 2003
Externally publishedYes

ASJC Scopus Subject Areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Keywords

  • cardiomyopathy
  • covariance inflation criterion
  • gene harvesting
  • lasso
  • least angle regression
  • microarray
  • model selection
  • support vector machine

Disciplines

  • Biology
  • Genetics

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