Abstract
Up to this point in the book, we focused on estimation and inference for monotone mean profiles. In Chaps. 7 and 8, the null hypothesis of no dose effect was tested against order alternatives and in Chaps. 9 and 10, we discussed two-stage clustering procedures for the subgroup of genes which were found to be significant. The ordered alternatives discussed in the previous chapters are called simple order alternatives, and the underlying assumption is that there is a monotone relationship between the dose and the mean gene expression. In this chapter, we discuss the case of testing the null hypothesis against order-restricted, but not necessarily monotone, alternatives assuming heteroscedastic variances. The order-restricted alternatives we consider in this chapter are the unimodal partial order (umbrella profiles) alternatives
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anraku, K. (1999). An information criterion for parameters under a simple order restriction. Biometrika, 86(1), 141–152(12).
Bretz, F., & Hothorn, L. A. (2003). Statistical analysis of monotone and non-monotone dose-response data from in vitro toxicological assays. ALTA 31, (Suppl. 1), 81–96, 2003, http://ecvam.jrc.it/publication/Bretz-Hothorn.pdf.
Gou, W. and Peddada, S. (2008), Adaptive choice of the number of bootstrap samples in large scale multiple testing, Statistical Applications in Genetics and Molecular Biology, 7(1).
Liu, T., Lin, N., Shi, N., & Zhang, B. (2009a). Order-restricted information criterion-based clustering algorithm. Reference manual. http://cran.r-project.org/web/packages/ORIClust/.
Liu, T., Lin, N., Shi, N., & Zhang, B. (2009b). Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments. BMC Bioinformatics, 10, 146.
Peddada, S., Lobenhofer, E. K., Li, L., Afshari, C. A., Weinberg, C. R., & Umbach, D. M. (2003). Gene selection and clustering for time-course and dose-response microarray experimants using order-restricted inference. Bioinformatics, 19(7), 834–841.
Peddada, S., Harris, S., & Harvey E. (2005). ORIOGEN: order restricted inference for ordered gene expression data. Bioinformatics, 21(20), 3933–3934.
Peddada, D. S., Umbach, M.D., & Harris, F.S. (2009). A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments. BMC Bioinformatics, 10, 438. http://www.biomedcentral.com/1471-2105/10/438
Peddada, S., Harris, S., & Davidov, O. (2010). Analysis of correlated gene expression data on ordered categories. Journal of Indian Society of Agricaltural Statistics, 64(1), 45–60.
Robertson, T., Wright, F. T., & Dykstra, R. L. (1988). Order restricted statistical inference. New York: Wiley.
Simmons, S. J., & Peddada, S. (2007). Order-restricted inference for ordered gene expresion (ORIOGEN) data under heteroscedastic variances. Bioinformation, 1(10), 414–419.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lin, D., Shkedy, Z. (2012). Beyond the Simple Order Alternatives. 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_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-24007-2_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24006-5
Online ISBN: 978-3-642-24007-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)