Abstract
Network Component Analysis (NCA) has shown its effectiveness in regulator identification by inferring the transcription factor activity (TFA) when both microarray data and ChIP-on-chip data are available. However, the NCA scheme is not applicable to many biological studies due to the lack of complete ChIP-on-chip data. In this paper, we propose an integrative NCA (iNCA) approach to combine motif information, limited ChIP-on-chip data, and gene expression data for regulatory network inference. Specifically, a Bayesian framework is adopted to develop a novel strategy, namely stability analysis with topological sampling, to infer key TFAs and their downstream gene targets. The iNCA approach with stability analysis reduces the computational cost by avoiding a direct estimation of the high-dimensional distribution in a traditional Bayesian approach. Stability indices are designed to measure the goodness of the estimated TFAs and their connectivity strengths. The approach can also be used to evaluate the confidence level of different data sources, considering the inevitable inconsistency among the data sources. The iNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that iNCA can effectively integrate motif information, ChIP-on-chip data and microarray data to identify key regulators and their gene targets in muscle regeneration. In particular, several identified TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments.
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References
Slonim, D.K.: From patterns to pathways: gene expression data analysis comes of age. Nat. Genet. 32, 502–508 (2002)
Segal, E., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34(2), 166–176 (2003)
Lee, S.I., Batzoglou, S.: Application of independent component analysis to microarrays. Genome. Biol. 4(11), 76 (2003)
Gong, T., et al.: Latent Variable and nICA Modeling of Pathway Gene Module Composite. In: Engineering in Medicine and Biology Society, 2006. EMBS 2006. 28th Annual International Conference of the IEEE, pp. 5872–5875 (2006)
Pascual-Montano, A., et al.: bioNMF: a versatile tool for non-negative matrix factorization in biology. BMC Bioinformatics 7, 366 (2006)
Liao, J.C., et al.: Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA 100(26), 15522–15527 (2003)
Chen, G., Jensen, S.T., Stoeckert Jr., C.J.: Clustering of genes into regulons using integrated modeling-COGRIM. Genome. Biol. 8(1), 4 (2007)
Sabatti, C., James, G.M.: Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics 22(6), 739–746 (2006)
Bakay, M., et al.: Nuclear envelope dystrophies show a transcriptional fingerprint suggesting disruption of Rb-MyoD pathways in muscle regeneration. Brain 129(Pt 4), 996–1013 (2006)
Lee, T.I., et al.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)
Yang, Y.L., et al.: Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics 6(1), 90 (2005)
Halees, A.S., Leyfer, D., Weng, Z.: PromoSer: A large-scale mammalian promoter and transcription start site identification service. Nucleic. Acids. Res. 31(13), 3554–3559 (2003)
Kel, A.E., et al.: MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic. Acids. Res. 31(13), 3576–3579 (2003)
Chekmenev, D.S., Haid, C., Kel, A.E.: P-Match: transcription factor binding site search by combining patterns and weight matrices. Nucleic Acids. Res. 33(Web Server issue), 432–437 (2005)
Matys, V., et al.: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids. Res. 34(Database issue), 108–110 (2006)
Lange, T., et al.: Stability-Based Model Selection. In: Advances in Neural Information Processing Systems (NIPS 2002) (2002)
Zhao, P., et al.: In vivo filtering of in vitro expression data reveals MyoD targets. C R Biol. 326(10-11), 1049–1065 (2003)
Blais, A., et al.: An initial blueprint for myogenic differentiation. Genes. Dev. 19(5), 553–569 (2005)
Walowitz, J.L., et al.: Proteolytic regulation of the zinc finger transcription factor YY1, a repressor of muscle-restricted gene expression. J. Biol. Chem. 273(12), 6656–6661 (1998)
Galvagni, F., et al.: The dystrophin promoter is negatively regulated by YY1 in undifferentiated muscle cells. J. Biol. Chem. 273(50), 33708–33713 (1998)
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Wang, C. et al. (2008). Integrative Network Component Analysis for Regulatory Network Reconstruction. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_19
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DOI: https://doi.org/10.1007/978-3-540-79450-9_19
Publisher Name: Springer, Berlin, Heidelberg
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