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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References
Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995)
Velculescu, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W.: Serial analysis of gene expression. Science 270, 484–487 (1995)
Bertelsen, A.H., Velculescu, V.E.: High-throughput gene expression analysis using SAGE. Drug Discovery Today 3, 152–159 (1998)
Baggerly, K.A., Deng, L., Morris, J.S., Aldaz, C.M.: Overdispersed logistic regression for SAGE: modelling multiple groups and covariates. BMC Bioinformatics 5, 144 (2004)
Ruijter, J.M., Van Kampen, A.H., Baas, F.: Statistical evaluation of SAGE libraries: consequences for experimental design. Physiol Genomics 11, 37–44 (2002)
Man, M.Z., Wang, X., Wang, Y.: POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 16, 953–959 (2000)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 1st edn. Chapman & Hall/CRC (1995)
Vencio, R.Z., Brentani, H., Patrao, D.F., Pereira, C.A.: Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE). BMC Bioinformatics 5, 119 (2004)
Kaufman, S., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Sturtz, S., Ligges, U., Gelman, A.: R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software, 12 (2005)
Kim, N., Shin, S., Lee, S.: ECgene: genome-based EST clustering and gene modeling for alternative splicing. Genome Res. 15, 566–576 (2005)
Friedl, P., Wolf, K.: Tumor-cell invasion and migration: Diversity and escape mechanisms. Nature Rev. Cancer 3, 362–374 (2003)
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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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