Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang

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

1 Scopus citations

Abstract

Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.

Original languageEnglish (US)
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages361-364
Number of pages4
DOIs
StatePublished - 2011
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: Jun 28 2011Jun 30 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Country/TerritoryFrance
CityNice
Period6/28/116/30/11

Keywords

  • Bayesian factor analysis
  • PCA
  • non-negative factor analysis
  • principle component analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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