Reconstruction of transcription regulatory networks by stability-based network component analysis

  • Authors:

  • Xi Chen;Chen Wang;Ayesha N. Shajahan;Rebecca B. Riggins;Robert Clarke;Jianhua Xuan

  • Affiliations:

  • Department of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Department of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC;Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC;Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC;Department of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA

  • Venue:
  • ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
  • Year:
  • 2012

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Abstract

Reliable inference of transcription regulatory networks is still a challenging task in the field of computational biology. Network component analysis (NCA) has become a powerful scheme to uncover the networks behind complex biological processes, especially when gene expression data is integrated with binding motif information. However, the performance of NCA is impaired by the high rate of false connections in binding motif information and the high level of noise in gene expression data. Moreover, in real applications such as cancer research, the performance of NCA in simultaneously analyzing multiple candidate transcription factors (TFs) is further limited by the small sample number of gene expression data. In this paper, we propose a novel scheme, stability-based NCA, to overcome the above-mentioned problems by addressing the inconsistency between gene expression data and motif binding information (i.e., prior network knowledge). This method introduces small perturbations on prior network knowledge and utilizes the variation of estimated TF activities to reflect the stability of TF activities. Such a scheme is less limited by the sample size and especially capable to identify condition-specific TFs and their target genes. Experiment results on both simulation data and real breast cancer data demonstrate the efficiency and robustness of the proposed method.