Abstract
In the Serial Analysis of Gene Expression (SAGE) analysis, the statistical procedures have been performed after aggregation of observations from the various libraries for the same class. Most studies have not accounted for the within-class variability. The identification of the differentially expressed genes based on the class separation has not been easy because of heteroscedasticity of libraries.We propose a hierarchical Bayesian model that accounts for the within-class variability. The differential expression is measured by a distribution-free silhouette width which was first introduced into the SAGE differential expression analysis. It is shown that the silhouette width is more appropriate and is easier to compute than the error rate.
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© 2006 Springer-Verlag Berlin Heidelberg
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Nam, S., Lee, S., Lee, S., Shin, S., Park, T. (2006). Bayesian Hierarchical Models for Serial Analysis of Gene Expression. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_4
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DOI: https://doi.org/10.1007/11960669_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68970-6
Online ISBN: 978-3-540-68971-3
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