Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic

• Published in: Bioinformatics 2012-08, Vol.28 (15), p.1990-1997
• Main Authors: JINGHUA GU, JIANHUA XUAN, RIGGINS, Rebecca B, LI CHEN, YUE WANG, CLARKE, Robert
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Breast Neoplasms - genetics; Cell Cycle - genetics; Cell Line, Tumor; Computational Biology - methods; Computer Simulation; Female; Fundamental and applied biological sciences. Psychology; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Original Papers; Signal Transduction - genetics; Signal-To-Noise Ratio; Software; Transcription Factors - genetics; Transcription Factors - metabolism
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/bts296

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. 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. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. [e-mail] Supplementary data are available at Bioinformatics online.