CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.06 - Nov.-Dec.
Issue No.06 - Nov.-Dec. (2013 vol.10)
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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.146
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