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

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article number6365177
Pages (from-to)1347-1358
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
StatePublished - Nov 2013
Externally publishedYes


  • Multivariate regression
  • Network component analysis
  • Stability analysis
  • Transcriptional regulatory network
  • t-statistic


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