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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2010

Open Access Proceedings

Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

Jia Meng1, Jianqiu (Michelle) Zhang1, Yidong Chen23 and Yufei Huang123*

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, USA

2 Department of Epidemiology and Biostatistics, UT Health Science Center at San Antonio, San Antonio, Texas, USA

3 Greehey Children’s Cancer Research Institute, UT Health Science Center at San Antonio, San Antonio, Texas, USA

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Proteome Science 2011, 9(Suppl 1):S9  doi:10.1186/1477-5956-9-S1-S9

Published: 14 October 2011

Abstract

Background

Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task.

Results

We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method.

Conclusions

The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.