A Bayesian method for analysing spotted microarray data

Colin D. Meiklejohn, Jeffrey P. Townsend

Research output: Contribution to journalReview article

18 Scopus citations

Abstract

In the decade since their invention, spotted microarrays have been undergoing technical advances that have increased the utility, scope and precision of their ability to measure gene expression. At the same time, more researchers are taking advantage of the fundamentally quantitative nature of these tools with refined experimental designs and sophisticated statistical analyses. These new approaches utilise the power of microarrays to estimate differences in gene expression levels, rather than just categorising genes as up- or down-regulated, and allow the comparison of expression data across multiple samples. In this review, some of the technical aspects of spotted microarrays that can affect statistical inference are highlighted, and a discussion is provided of how several methods for estimating gene expression level across multiple samples deal with these challenges. The focus is on a Bayesian analysis method, BAGEL, which is easy to implement and produces easily interpreted results.

Original languageEnglish (US)
Pages (from-to)318-330
Number of pages13
JournalBriefings in bioinformatics
Volume6
Issue number4
DOIs
StatePublished - Dec 1 2005

Keywords

  • Bayesian analysis
  • Experimental design
  • Gene expression
  • Markov chain
  • Microarray
  • Monte Carlo
  • Statistical power
  • cDNA

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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