Abstract
Common and important tasks arising in microarray experiments are the identification of differentially expressed genes and the classification of biological samples. The SAM (Significance Analysis of Microarrays) procedure is a widely used method for dealing with the multiple testing problem concerned with the former task, whereas the PAM (Prediction Analysis of Microarrays) procedure is a method that can cope with the problems associated with the latter task.
In this presentation, we show how these two procedures developed for analyzing continuous gene expression data can be modified for the analysis of categorical SNP (Single Nucleotide Polymorphism) data.
This work has been supported by the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 475.
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Schwender, H. (2005). Modifying Microarray Analysis Methods for Categorical Data — SAM and PAM for SNPs. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_42
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DOI: https://doi.org/10.1007/3-540-28084-7_42
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