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


CONCLUSIONS
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.
Beasley
T Mark
TM
Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.
Holt
Janet K
JK
Allison
David B
DB
D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016014:Linear Models; D015233:Models, Statistical; D015336:Molecular Probe Techniques; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010597:Pharmacogenetics; D012333:RNA, Messenger / Q000235:genetics
D012333:RNA, Messenger