Comparison of linear weighting schemes for perfect match and mismatch gene expression levels from microarray data.

Academic Article

Abstract

  • BACKGROUND: Data analytic approaches to Affymetrix microarray data include: (a) a covariate model, in which the observed signal is some estimated linear function of perfect match (PM) and mismatch (MM) signals; (b) a difference model [PM-MM]; and (c) a PM-only model, in which MM data is not utilized. METHODS: By decomposing the correlations among the variables in the statistical model and making certain assumptions, we theoretically derive the statistical model that reflects the actual gene expression level under a variety of conditions expected in microarray data. RESULTS AND CONCLUSION: When modeling non-systematic variation, the covariate model provides maximum flexibility and often reflects the actual gene expression levels better than the difference model. However, the PM-only model demonstrates superior power in an overwhelming majority of realistic situations, which provides theoretical support for the current trend to employ PM-only models in microarray data analyzes.
  • Published In

    Keywords

  • Analysis of Variance, Data Interpretation, Statistical, Gene Expression Profiling, Linear Models, Models, Statistical, Molecular Probe Techniques, Oligonucleotide Array Sequence Analysis, Pharmacogenetics, RNA, Messenger
  • Author List

  • Beasley TM; Holt JK; Allison DB
  • Start Page

  • 197
  • End Page

  • 205
  • Volume

  • 5
  • Issue

  • 3