The Community for Technology Leaders
RSS Icon
Issue No.06 - Nov.-Dec. (2013 vol.10)
pp: 1347-1358
Xi Chen , Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA
Jianhua Xuan , Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA
Chen Wang , Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA
Ayesha N. Shajahan , Dept. of Oncology, Georgetown Univ. Med. Center, Washington, DC, USA
Rebecca B. Riggins , Dept. of Oncology, Georgetown Univ. Med. Center, Washington, DC, USA
Robert Clarke , Dept. of Oncology, Georgetown Univ. Med. Center, Washington, DC, USA
Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performance of NCA is impaired by the high rate of false connections in binding information. In this paper, we integrate stability analysis with NCA to form a novel scheme, namely stability-based NCA (sNCA), for regulatory network identification. The method mainly addresses the inconsistency between gene expression data and binding motif information. Small perturbations are introduced to prior regulatory network, and the distance among multiple estimated transcript factor (TF) activities is computed to reflect the stability for each TF's binding network. For target gene identification, multivariate regression and t-statistic are used to calculate the significance for each TF-gene connection. Simulation studies are conducted and the experimental results show that sNCA can achieve an improved and robust performance in TF identification as compared to NCA. The approach for target gene identification is also demonstrated to be suitable for identifying true connections between TFs and their target genes. Furthermore, we have successfully applied sNCA to breast cancer data to uncover the role of TFs in regulating endocrine resistance in breast cancer.
Gene expression, Stability analysis, Network component analysis, Computational biology, Bioinformatics, Regression analysis,t-statistic, Transcriptional regulatory network, network component analysis, stability analysis, multivariate regression
Xi Chen, Jianhua Xuan, Chen Wang, Ayesha N. Shajahan, Rebecca B. Riggins, Robert Clarke, "Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 6, pp. 1347-1358, Nov.-Dec. 2013, doi:10.1109/TCBB.2012.146
[1] R. De Smet and K. Marchal, "Advantages and Limitations of Current Network Inference Methods," Nature Rev. Microbiology, vol. 8, no. 10, pp. 717-29, Oct. 2010.
[2] E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller, and N. Friedman, "Module Networks: Identifying Regulatory Modules and Their Condition-Specific Regulators from Gene Expression Data," Nature Genetics, vol. 34, no. 2, pp. 166-76, June 2003.
[3] E. Segal, D. Pe'er, A. Regev, D. Koller, and N. Friedman, "Learning Module Networks," J. Machine Learning Research, vol. 6, pp. 557-588, 2005.
[4] T.I. Lee, N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L. Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford, and R.A. Young, "Transcriptional Regulatory Networks in Saccharomyces Cerevisiae," Science, vol. 298, no. 5594, pp. 799-804, Oct. 2002.
[5] Z. Bar-Joseph, G.K. Gerber, T.I. Lee, N.J. Rinaldi, J.Y. Yoo, F. Robert, D.B. Gordon, E. Fraenkel, T.S. Jaakkola, R.A. Young, and D.K. Gifford, "Computational Discovery of Gene Modules and Regulatory Networks," Nature Biotechnology, vol. 21, no. 11, pp. 1337-1342, Nov. 2003.
[6] E. Segal, T. Raveh-Sadka, M. Schroeder, U. Unnerstall, and U. Gaul, "Predicting Expression Patterns from Regulatory Sequence in Drosophila Segmentation," Nature, vol. 451, no. 7178, pp. 535-540, Jan. 2008.
[7] T. Gong, J. Xuan, L. Chen, R.B. Riggins, H. Li, E.P. Hoffman, R. Clarke, and Y. Wang, "Motif-Guided Sparse Decomposition of Gene Expression Data for Regulatory Module Identification," BMC Bioinformatics, vol. 12, p. 82, 2011.
[8] C. van Waveren and C.T. Moraes, "Transcriptional Co-Expression and Co-Regulation of Genes Coding for Components of the Oxidative Phosphorylation System," BMC Genomics, vol. 9, p. 18, 2008.
[9] D.T. Chang, C.Y. Huang, C.Y. Wu, and W.S. Wu, "YPA: An Integrated Repository of Promoter Features in Saccharomyces Cerevisiae," Nucleic Acids Research, vol. 39, no. database issue, pp. D647-D652, Jan. 2011.
[10] J.C. Liao, R. Boscolo, Y.L. Yang, L.M. Tran, C. Sabatti, and V.P. Roychowdhury, "Network Component Analysis: Reconstruction of Regulatory Signals in Biological Systems," Proc Nat'l Academy of Sciences of USA, vol. 100, no. 26, pp. 15522-15527, Dec. 2003.
[11] C. Chang, Z. Ding, Y.S. Hung, and P.C. Fung, "Fast Network Component Analysis (FastNCA) for Gene Regulatory Network Reconstruction from Microarray Data," Bioinformatics, vol. 24, no. 11, pp. 1349-1358, June 2008.
[12] L. Chen, J. Xuan, R.B. Riggins, Y. Wang, E.P. Hoffman, and R. Clarke, "Multilevel Support Vector Regression Analysis to Identify Condition-Specific Regulatory Networks," Bioinformatics, vol. 26, no. 11, pp. 1416-1422, June 2010.
[13] S.J. Galbraith, L.M. Tran, and J.C. Liao, "Transcriptome Network Component Analysis with Limited Microarray Data," Bioinformatics, vol. 22, no. 15, pp. 1886-1894, Aug. 2006.
[14] D.H. Nguyen and P. D'Haeseleer, "Deciphering Principles of Transcription Regulation in Eukaryotic Genomes," Molecular Systems Biology, vol. 2, p. 20060012, 2006.
[15] T.Y. Chien, C.K. Lin, C.W. Lin, Y.Z. Weng, C.Y. Chen, and D.T. Chang, "DBD2BS: Connecting a DNA-Binding Protein with Its Binding Sites," Nucleic Acids Research, vol. 40, no. web server issue, pp. W173-W179, July 2012.
[16] S. Mukherjee, P. Niyogi, T. Poggio, and R. Rifkin, "Learning Theory: Stability Is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization," Advances in Computational Math., vol. 5, pp. 161-193, 2006.
[17] O. Bousquet and A. Elisseeff, "Stability and Generalization," J. Machine Learning Research, vol. 2, pp. 499-526, 2006.
[18] R.M. Dudley, E. Gine, and J. Zinn, "Uniform and Universal Glivenko-CAntelli Classes," J. Theoretical Probability, vol. 4, pp. 485-510, 1991.
[19] D.C. Montgomery, E.A. Peck, and G.G. Vining, Introduction to Linear Regression Analysis, fifth ed., pp. 67-104. Wiley, 2011.
[20] I.T. Jolliffe, Principal Component Analysis, second ed., pp. 167-198. Springer, 2002.
[21] A.E. Kel, E. Gossling, I. Reuter, E. Cheremushkin, O.V. Kel-Margoulis, and E. Wingender, "MATCH: A Tool for Searching Transcription Factor Binding Sites in DNA Sequences," Nucleic Acids Research, vol. 31, no. 13, pp. 3576-3579, July 2003.
[22] V. Matys, O.V. Kel-Margoulis, E. Fricke, I. Liebich, S. Land, A. Barre-Dirrie, I. Reuter, D. Chekmenev, M. Krull, K. Hornischer, N. Voss, P. Stegmaier, B. Lewicki-Potapov, H. Saxel, A.E. Kel, and E. Wingender, "TRANSFAC and Its Module TRANSCompel: Transcriptional Gene Regulation in Eukaryotes," Nucleic Acids Research, vol. 34, no. database issue, pp. D108-D110, Jan. 2006.
[23] S. Loi, B. Haibe-Kains, C. Desmedt, F. Lallemand, A.M. Tutt, C. Gillet, P. Ellis, A. Harris, J. Bergh, J.A. Foekens, J.G. Klijn, D. Larsimont, M. Buyse, G. Bontempi, M. Delorenzi, M.J. Piccart, and C. Sotiriou, "Definition of Clinically Distinct Molecular Subtypes in Estrogen Receptor-Positive Breast Carcinomas through Genomic Grade," J. Clinical Oncology, vol. 25, no. 10, pp. 1239-1246, Apr. 2007.
[24] W.F. Symmans, C. Hatzis, C. Sotiriou, F. Andre, F. Peintinger, P. Regitnig, G. Daxenbichler, C. Desmedt, J. Domont, C. Marth, S. Delaloge, T. Bauernhofer, V. Valero, D.J. Booser, G.N. Hortobagyi, and L. Pusztai, "Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer," J. Clinical Oncology, vol. 28, no. 27, pp. 4111-4119, Sept. 2010.
[25] C.J. Creighton, K.E. Cordero, J.M. Larios, R.S. Miller, M.D. Johnson, A.M. Chinnaiyan, M.E. Lippman, and J.M. Rae, "Genes Regulated by Estrogen in Breast Tumor Cells in Vitro Are Similarly Regulated in Vivo in Tumor Xenografts and Human Breast Tumors," Genome Biology, vol. 7, no. 4, p. R28, 2006.
[26] S.A. Vlahopoulos, S. Logotheti, D. Mikas, A. Giarika, V. Gorgoulis, and V. Zoumpourlis, "The Role of ATF-2 in Oncogenesis," Bioessays, vol. 30, no. 4, pp. 314-327, Apr. 2008.
[27] T. Maekawa, T. Shinagawa, Y. Sano, T. Sakuma, S. Nomura, K. Nagasaki, Y. Miki, F. Saito-Ohara, J. Inazawa, T. Kohno, J. Yokota, and S. Ishii, "Reduced Levels of ATF-2 Predispose Mice to Mammary Tumors," Molecular Cellular Biology, vol. 27, no. 5, pp. 1730-1744, Mar. 2007.
[28] J.C. Hsu, R. Bravo, and R. Taub, "Interactions among LRF-1, JunB, c-Jun, and c-Fos Define a Regulatory Program in the G1 Phase of Liver Regeneration," Molecular Cellular Biology, vol. 12, no. 10, pp. 4654-4665, Oct. 1992.
[29] Z. Odrowaz and A.D. Sharrocks, "ELK1 Uses Different DNA Binding Modes to Regulate Functionally Distinct Classes of Target Genes," PLoS Genetics, vol. 8, no. 5, p. e1002694, 2012.
[30] C.V. Dang, "c-Myc Target Genes Involved in Cell Growth, Apoptosis, and Metabolism," Molecular Cellular Biology, vol. 19, no. 1, pp. 1-11, Jan. 1999.
[31] C.M. McNeil, C.M. Sergio, L.R. Anderson, C.K. Inman, S.A. Eggleton, N.C. Murphy, E.K. Millar, P. Crea, J.G. Kench, M.C. Alles, M. Gardiner-Garden, C.J. Ormandy, A.J. Butt, S.M. Henshall, E.A. Musgrove, and R.L. Sutherland, "c-Myc Overexpression and Endocrine Resistance in Breast Cancer," J. Steroid Biochemistry Molecular Biology, vol. 102, nos. 1-5, pp. 147-155, Dec. 2006.
[32] T.W. Miller, J.M. Balko, Z. Ghazoui, A. Dunbier, H. Anderson, M. Dowsett, A.M. Gonzalez-Angulo, G.B. Mills, W.R. Miller, H. Wu, Y. Shyr, and C.L. Arteaga, "A Gene Expression Signature from Human Breast Cancer Cells with Acquired Hormone Independence Identifies MYC as a Mediator of Antiestrogen Resistance," Clinical Cancer Research, vol. 17, no. 7, pp. 2024-2034, Apr. 2011.
[33] J.C. Yao, L. Wang, D. Wei, W. Gong, M. Hassan, T.T. Wu, P. Mansfield, J. Ajani, and K. Xie, "Association between Expression of Transcription Factor Sp1 and Increased Vascular Endothelial Growth Factor Expression, Advanced Stage, and Poor Survival in Patients with Resected Gastric Cancer," Clinical Cancer Research, vol. 10, no. 12, pp. 4109-4117, June 2004.
[34] S. Safe and M. Abdelrahim, "Sp Transcription Factor Family and Its Role in Cancer," European J. Cancer, vol. 41, no. 16, pp. 2438-2448, Nov. 2005.
[35] J.L. Schwartz, A.N. Shajahan, and R. Clarke, "The Role of Interferon Regulatory Factor-1 (IRF1) in Overcoming Antiestrogen Resistance in the Treatment of Breast Cancer," Int'l J. Breast Cancer, vol. 2011, p. 912102, 2011.
[36] P.J. Klover, W.J. Muller, G.W. Robinson, R.M. Pfeiffer, D. Yamaji, and L. Hennighausen, "Loss of STAT1 from Mouse Mammary Epithelium Results in an Increased Neu-Induced Tumor Burden," Neoplasia, vol. 12, no. 11, pp. 899-905, Nov. 2010.
[37] J. Turkson, "STAT Proteins as Novel Targets for Cancer Drug Discovery," Expert Opinion Therapeutic Targets, vol. 8, no. 5, pp. 409-422, Oct. 2004.
[38] S.H. Tan and M.T. Nevalainen, "Signal Transducer and Activator of Transcription 5A/B in Prostate and Breast Cancers," Endocrine Related Cancer, vol. 15, no. 2, pp. 367-390, June 2008.
[39] J.L. Gooch, B. Christy, and D. Yee, "STAT6 Mediates Interleukin-4 Growth Inhibition in Human Breast Cancer Cells," Neoplasia, vol. 4, no. 4, pp. 324-31, July/Aug. 2002.
[40] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed. Wiley, 2001.
[41] D. Ficenec, M. Osborne, J. Pradines, D. Richards, R. Felciano, R.J. Cho, R.O. Chen, T. Liefeld, J. Owen, A. Ruttenberg, C. Reich, J. Horvath, and T. Clark, "Computational Knowledge Integration in Biopharmaceutical Research," Briefings in Bioinformatics, vol. 4, no. 3, pp. 260-278, Sept. 2003.
[42] D.T. Chang, T.J. Yao, C.Y. Fan, C.Y. Chiang, and Y.H. Bai, "AH-DB: Collecting Protein Structure Pairs before and after Binding," Nucleic Acids Research, vol. 40, no. database issue, pp. D472-D478, Jan. 2012.
134 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool