Abstract
In Chaps. and 8, we discussed several testing procedures to detect differentially expressed genes with monotone relationship with respect to dose. The second question of primary interest in dose-response studies is the nature (or the shape of curve) of the dose-response relationship. In the context of dose-response microarray experiments, we wish to group (or classify) genes with similar dose-response relationship. Similar to the previous chapters, the subset of genes with monotone relationship is of interest.
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References
Amaratunga, D., Cabrera, J., & Kovtun, V. (2008). Microarray learning with ABC. Biostatistics, 9, 128–136.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of Royal Statistical Soceity B, 57, 289–300.
Breiman, L. (1996) Random forests. Machine Learning, 24, 123–140.
Calinski, R. B., & Harabasz, J. A. (1974). Dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.
Cheng, Y., & Church, G. M. (2000). Biclustering of expression data. Proceedings of the Conference on Intelligent Systems for Molecular Biology, 55, 93–104.
Ge, Y., Dudoit, S., & Speed, P. T. (2003). Resampling based multiple testing for microarray data analysis (Technical report, 633). Berkeley: University of Berkeley.
Hartigan, J. A., & Wong, M. A, (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics), 28(1), 100–108.
Johnson, R. A., and Wichern, D. W.(2008). Applied Multivariate statistical analysis. Pearson.
Kohonen, T. (2001), Self-Organizing Maps. 3rd edition, Springer-verlag, Berlin.
Lin, D., Shkedy, Z., Yekutieli, D., Burzykowki, T., Göhlmann, H. W. H., De Bondt, A., et al. (2007). Testing for trend in dose-response microarray experiments: Comparison of several testing procedures, multiplicity, and resampling-based inference. Statistical Application in Genetics and Molecular Biology, 6(1). Article 26.
Madeira, S. C., & Oliviera, A. L. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 1(1), 24–45.
Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., et al. (2006). Systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22(9), 1122–1129.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society B, 63, 411–423.
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Kasim, A. et al. (2012). δ-Clustering of Monotone Profiles. In: Lin, D., Shkedy, Z., Yekutieli, D., Amaratunga, D., Bijnens, L. (eds) Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Use R!. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24007-2_9
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DOI: https://doi.org/10.1007/978-3-642-24007-2_9
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