title: Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.
abstract: BACKGROUND
  Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context.
  
  
  RESULTS
  In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.
  
  
  METHODS
  The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm.
  
  
  BACKGROUND
  xuan@vt.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.
authors.forename: Jinghua
authors.initials: J
authors.lastname: Gu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Rebecca B
authors.initials: RB
authors.lastname: Riggins
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D002453:Cell Cycle / Q000235:genetics; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D015398:Signal Transduction / Q000235:genetics; D059629:Signal-To-Noise Ratio; D012984:Software; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
chemical_list: D014157:Transcription Factors