Abstract
Identification of coordinate gene expression changes across phenotypes or biological conditions is the basis of the ability to decode the role of gene expression regulatory networks. Statistically, the identification of these changes can be viewed as a search for groups (most typically pairs) of genes whose expression provides better phenotype discrimination when considered jointly than when considered individually. Such groups are defined as being jointly differentially expressed. In this chapter several approaches for identifying jointly differentially expressed groups of genes are reviewed of compared on a set of simulations.
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
Schena, M. (2000) Microarray Biochip Technology. BioTechniques Press, Westborough, MA.
Pan, W. (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18, 546–554.
Parmigiani, G., Garrett, E. S., Irizarry, R. A., and Zeger, S. L. (eds.) (2003) The analysis of gene expression data: an overview of methods and software. Springer, New York, 1–20.
Xiao, Y., Frisina, R., Gordon, A., Klebanov, L., and Yakovlev, A. (2004) Multivariate search for differentially expressed gene combinations. BMC Bioinformatics 5, 164.
Shedden, K. and Taylor, J. (2004) Differential correlation detects complex associations between gene expression and clinical outcomes in lung adenocarcinomas. Methods Microarray Data Anal. IV, 121–132.
Dettling, M., Gabrielson, E., and Parmigiani, G. (2005) Searching for differentially expressed gene combinations. Genome Biol. 6(10), R88.
Li, K. C. (2002) Genome-wide coexpression dynamics: Theory and application. Proc. Natl. Acad. Sci. 16,875–16,880.
Li, K. C., Liu, C. T., Sun, W., Yuan, S., and Yu, T. (2004) A system for enhancing genome-wide coexpression dynamics study. Proc. Natl. Acad. Sci. USA 101(44), 15,561–15,566.
Lai, Y., Wu, B., Chen, L., and Zhao, H. (2004) A statistical method for identifying differential gene-gene co-expression patterns. Bioinformatics 20, 3146–3155.
Kostka, D. and Spang, R. (2004) Finding disease specific alterations in the co-expression of genes. Bioinformatics 20(Suppl 1), i194–i199.
Friedman, N., Linial, M., Nachman, I., and Pe’er, D. (2000) Using Bayesian networks to analyze expression data. J. Comput. Biol. 7(3–4), 601–620.
Tomlins, S. A., Rhodes, D. R., Perner, S., et al. (2005) Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310(5748), 644–648.
Box, G. E. P., Hunter, W. G., and Hunter, J. S. (1978) Statistics for experimenters: An introduction to design, data analysis, and model building. Wiley, New York.
Kerr, M. K., Martin, M., and Churchill, G. A. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol. 7(6), 819–837.
Shannon, C. (1948) A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423.
Szabo, A., Boucher, K., Carroll, W. L., Klebanov, L. B., Tsodikov, A. D., and Yakovlev, A. Y. (2002) Variable selection and pattern recognition with gene expression data generated by the microarray technology. Math. Biosci. 176(1), 71–98.
Szabo, A., Boucher, K., Jones, D., Tsodikov, A. D., Klebanov, L. B., and Yakovlev, A. Y. (2003) Multivariate exploratory tools for microarray data analysis. Biostatistics 4(4), 555–567.
Cheng, Y. and Church, G. M. Biclustering of expression data. 93–103.
Heckerman, D. (1995) A tutorial on learning with bayesian networks. Tech. rep., Microsoft Research, Redmond, Washington. Revised June 96.
Friedman, N. (2003) Probabilistic models for identifying regulation networks. Bioinformatics 19(Suppl 2), 1157.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Humana Press Inc.
About this protocol
Cite this protocol
Ho, YY., Cope, L., Dettling, M., Parmigiani, G. (2007). Statistical Methods for Identifying Differentially Expressed Gene Combinations. In: Ochs, M.F. (eds) Gene Function Analysis. Methods in Molecular Biology™, vol 408. Humana Press. https://doi.org/10.1007/978-1-59745-547-3_10
Download citation
DOI: https://doi.org/10.1007/978-1-59745-547-3_10
Publisher Name: Humana Press
Print ISBN: 978-1-58829-734-1
Online ISBN: 978-1-59745-547-3
eBook Packages: Springer Protocols