A Bayesian method for analysing spotted microarray data

Brief Bioinform. 2005 Dec;6(4):318-30. doi: 10.1093/bib/6.4.318.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Algorithms*
  • Bayes Theorem
  • Computer Simulation
  • Gene Expression Profiling / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • In Situ Hybridization, Fluorescence / methods*
  • Microscopy, Fluorescence / methods*
  • Models, Genetic*
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis / methods*
  • Software