Comparison of Linear Weighting Schemes for Perfect Match and Mismatch Gene Expression Levels from Microarray Data
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)
Publisher: Adis International
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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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