Abstract
An environmental stress response gene should, by definition, have common properties in its behavior across different stress treatments. We search for such common properties by models that maximize common variation, and explore potential regulators of the stress response by further maximizing mutual information with transcription factor binding data. A computationally tractable combination of generalized canonical correlations and clustering that searches for dependencies is proposed and shown to find promising sets of genes and their potential regulators.
This work was supported by the Academy of Finland, decisions 79017 and 207467. We wish to thank Eerika Savia for insights about gCCA.
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
Preview
Unable to display preview. Download preview PDF.
References
Bach, F.R., Jordan, M.I.: Kernel independent component analysis. JMLR 3, 1–48 (2002)
Causton, H.C., et al.: Remodeling of yeast genome expression in response to environmental changes. Molecular Biology of Cell 12, 323–337 (2001)
Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)
Gasch, P., et al.: Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell 11, 4241–4257 (2000)
Good, J.: On the application of symmetric Dirichlet distributions and their mixtures to contingency tables. Annals of Statistics 4(6), 1159–1189 (1976)
Hosack, D.A., et al.: Identifying biological themes within lists of genes with ease. Genome Biology, 4(R70) (2003)
Kerr, M.K., Churchill, G.A.: Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments. PNAS 98, 8961–8965 (2001)
Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58(3), 433–451 (1971)
Lee, T.I., et al.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002)
Sinkkonen, J., Nikkilä, J., Lahti, L., Kaski, S.: Associative clustering by maximizing a Bayes factor. Technical Report A68, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland (2003)
Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. In: 37th Annual Allerton Conference on Communication, Control, and Computing, Urbana, Illinois, pp. 368–377 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nikkilä, J., Roos, C., Kaski, S. (2004). Exploring Dependencies Between Yeast Stress Genes and Their Regulators. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive