Uncovering transcriptional regulatory networks by sparse Bayesian factor model

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes. The model admits prior knowledge from existing database regarding TF regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the Breast cancer microarray data of patients with Estrogen Receptor positive ER+ status and Estrogen Receptor negative ER- status, respectively.

Original languageEnglish (US)
Title of host publicationICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings
Pages1785-1788
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 IEEE 10th International Conference on Signal Processing, ICSP2010 - Beijing, China
Duration: Oct 24 2010Oct 28 2010

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP

Other

Other2010 IEEE 10th International Conference on Signal Processing, ICSP2010
CountryChina
CityBeijing
Period10/24/1010/28/10

Keywords

  • Bayesian sparse factor model
  • Component
  • Correlated non-negative factor
  • Dirichlet process mixture (DPM)
  • Gibbs sampling
  • Rectified Gaussian mixture
  • Transcriptional regulatory network

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Meng, J., Zhang, J., Chen, Y., & Huang, Y. (2010). Uncovering transcriptional regulatory networks by sparse Bayesian factor model. In ICSP2010 - 2010 IEEE 10th International Conference on Signal Processing, Proceedings (pp. 1785-1788). [5656704] (International Conference on Signal Processing Proceedings, ICSP). https://doi.org/10.1109/ICOSP.2010.5656704