IngentaConnect Comparison of Linear Weighting Schemes for Perfect Match and Mism...

Authors: Beasley, T. Mark¹; Holt, Janet K.²; Allison, David B.¹

Source: American Journal of PharmacoGenomics, Volume 5, Number 3, 2005 , pp. 197-205(9)

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.

Keywords: Bioinformatics; Statistics

Document Type: Research article

Affiliations: 1: 1 Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA 2: 2 Department of Educational Technology, Research & Assessment, Northern Illinois University, DeKalb, Alabama, USA

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Comparison of Linear Weighting Schemes for Perfect Match and Mismatch Gene Expression Levels from Microarray Data