Abstract
The construction of gene regulatory models from microarray time-series data has received much attention. Here we propose a method that extends standard correlation networks to incorporate explicit time-slices. The method is applied to a time-series dataset of a study on gene expression in the developmental phase of zebrafish. Results show that the method is able to distinguish real relations between genes from the data. These relations are explicitly placed in time, allowing for a better understanding of gene regulation. The method and data normalisation procedure have been implemented using the R statistical language and are available from http://zebrafish.liacs.nl/supplements.html .
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References
Wu, X., Ye, Y., Subramanian, K.: Interactive analysis of gene interactions using graphical Gaussian model. In: ACM SIGKDD Workshop on Data Mining in Bioinformatics, vol. 3, pp. 63–69 (2003)
Schfer, J., Strimmer, K.: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6), 754–764 (2005)
Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)
Kellam, P., Liu, X., Martin, N., Orengo, C., Swift, S., Tucker, A.: A framework for modelling virus gene expression data. Intelligent Data Analysis 6(3), 267–279 (2002)
Kim, S.Y., Imoto, S., Miyano, S.: Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform. 4(3), 228–235 (2003)
Chickering, D., Geiger, D., Heckerman, D.: Learning Bayesian networks is NP-hard. Technical Report MSR-TR-94-17, Microsoft Research (1994)
Zou, M., Conzen, S.D.: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21(1), 71–79 (2005)
Hill, A.: The environment and disease: association or causation? Proc. R. Soc. Med. 58, 295–300 (1965)
Phillips, C., Goodman, K.: The missed lessons of Sir Austin Bradford Hill. Epidemiologic Perspectives & Innovations 1(3) (2004)
Linney, E., Dobbs-McAuliffe, B., Sajadi, H., Malek, R.L.: Microarray gene expression profiling during the segmentation phase of zebrafish development. Comp. Biochem. Physiol. C. Toxicol Pharmacol. 138(3), 351–362 (2004)
Wit, E., McClure, J.: Statistics for microarrays: design, analysis and inference. John Wiley & Sons Ltd., Chichester (2004)
Pavlidis, P.: Using ANOVA for gene selection from microarray studies of the nervous system. Methods 31(4), 282–289 (2003)
Duboule, D.: The vertebrate limb: a model system to study the Hox/HOM gene network during development and evolution. Bioessays 14(6), 375–384 (1992)
Krumlauf, R.: Hox genes in vertebrate development. Cell 78(2), 191–201 (1994)
Prince, V.E., Moens, C.B., Kimmel, C.B., Ho, R.K.: Zebrafish hox genes: expression in the hindbrain region of wild-type and mutants of the segmentation gene, valentino. Development 125(3), 393–406 (1998)
Thummel, R., Li, L., Tanase, C., Sarras, M.P., Godwin, A.R.: Differences in expression pattern and function between zebrafish hoxc13 orthologs: recruitment of Hoxc13b into an early embryonic role. Dev. Biol. 274(2), 318–333 (2004)
Prince, V.E., Joly, L., Ekker, M., Ho, R.K.: Zebrafish hox genes: genomic organization and modified colinear expression patterns in the trunk. Development 125(3), 407–420 (1998)
Bel-Vialar, S., Itasaki, N., Krumlauf, R.: Initiating Hox gene expression: in the early chick neural tube differential sensitivity to FGF and RA signaling subdivides the HoxB genes in two distinct groups. Development 129(22), 5103–5115 (2002)
Forlani, S., Lawson, K.A., Deschamps, J.: Acquisition of Hox codes during gastrulation and axial elongation in the mouse embryo. Development 130(16), 3807–3819 (2003)
Gaunt, S.J., Strachan, L.: Temporal colinearity in expression of anterior Hox genes in developing chick embryos. Dev. Dyn. 207(3), 270–280 (1996)
Duboule, D., Doll, P.: The structural and functional organization of the murine HOX gene family resembles that of Drosophila homeotic genes. EMBO J. 8(5), 1497–1505 (1989)
Graham, A., Papalopulu, N., Krumlauf, R.: The murine and Drosophila homeobox gene complexes have common features of organization and expression. Cell 57(3), 367–378 (1989)
Duboule, D.: Vertebrate hox gene regulation: clustering and/or colinearity? Curr. Opin. Genet. Dev. 8(5), 514–518 (1998)
Kmita, M., Duboule, D.: Organizing axes in time and space; 25 years of colinear tinkering. Science 301(5631), 331–333 (2003)
de la Fuente, A., Bing, N., Hoeschele, I., Mendes, P.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 3565–3574 (2004)
Kimmel, C.B., Ballard, W.W., Kimmel, S.R., Ullmann, B., Schilling, T.F.: Stages of embryonic development of the zebrafish. Dev. Dyn. 203(3), 253–310 (1995)
Verbeek, F.J., Lawson, K.A., Bard, J.B.: Developmental bioinformatics: linking genetic data to virtual embryos. Int. J. Dev. Biol. 43(7), 761–771 (1999)
Welten, M., De Haan, S., Bertens, L., Noordermeer, J., Lamers, G., Spaink, H., Meijer, A., Verbeek, F.: ZebraFISH: Fluorescent in situ hybridization protocol and 3D images of gene expression patterns. Zebrafish (accepted, 2006)
Knosp, W.M., Scott, V., Bchinger, H.P., Stadler, H.S.: HOXA13 regulates the expression of bone morphogenetic proteins 2 and 7 to control distal limb morphogenesis. Development 131(18), 4581–4592 (2004)
Sakaguchi, S., Nakatani, Y., Takamatsu, N., Hori, H., Kawakami, A., Inohaya, K., Kudo, A.: Medaka unextended-fin mutants suggest a role for Hoxb8a in cell migration and osteoblast differentiation during appendage formation. Dev. Biol. 293(2), 426–438 (2006)
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Meuleman, W., Welten, M.C.M., Verbeek, F.J. (2006). Construction of Correlation Networks with Explicit Time-Slices Using Time-Lagged, Variable Interval Standard and Partial Correlation Coefficients. In: R. Berthold, M., Glen, R.C., Fischer, I. (eds) Computational Life Sciences II. CompLife 2006. Lecture Notes in Computer Science(), vol 4216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875741_23
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DOI: https://doi.org/10.1007/11875741_23
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