Motif-directed network component analysis for regulatory network inference

Chen Wang, Jianhua Xuan, Li Chen, Po Zhao, Yue Wang, Robert Clarke, Eric Hoffman

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Background: Network Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies due to limited topology information available, such as lack of ChIP-on-chip data. We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks. Results: We develop motif-directed NCA (mNCA) to incorporate motif information into NCA for regulatory network inference. While motif information is readily available from knowledge databases, it is a "noisy" source of network topology information consisting of many false positives. To overcome this problem, we develop a stability analysis procedure embedded in mNCA to resolve the inconsistency between motif information and gene expression data, and to enable the identification of stable TFAs. The mNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that the inferred TFAs are not only numerically stable but also biologically relevant to muscle differentiation process. In particular, several inferred TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments. Conclusion: A novel computational approach, mNCA, has been developed to integrate motif information and gene expression data for regulatory network reconstruction. Specifically, motif analysis is used to obtain initial network topology, and stability analysis is developed and applied with mNCA to extract stable TFAs. Experimental results on muscle regeneration microarray data have demonstrated that mNCA is a practical and reliable computational method for regulatory network inference and pathway discovery.

Original languageEnglish (US)
Article numberS21
JournalBMC Bioinformatics
Volume9
Issue numberSUPPL. 1
DOIs
StatePublished - Feb 13 2008
Externally publishedYes

Fingerprint

Network components
Directed Network
Transcription factors
Regulatory Networks
Transcription Factors
Transcription Factor
Microarrays
Gene expression
Muscle
Information Services
Chip
Gene Expression Data
Topology
Microarray Data
Gene Expression
Muscles
Regeneration
Myogenin
Network Topology
Stability Analysis

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Medicine(all)
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Wang, C., Xuan, J., Chen, L., Zhao, P., Wang, Y., Clarke, R., & Hoffman, E. (2008). Motif-directed network component analysis for regulatory network inference. BMC Bioinformatics, 9(SUPPL. 1), [S21]. https://doi.org/10.1186/1471-2105-9-S1-S21

Motif-directed network component analysis for regulatory network inference. / Wang, Chen; Xuan, Jianhua; Chen, Li; Zhao, Po; Wang, Yue; Clarke, Robert; Hoffman, Eric.

In: BMC Bioinformatics, Vol. 9, No. SUPPL. 1, S21, 13.02.2008.

Research output: Contribution to journalArticle

Wang, C, Xuan, J, Chen, L, Zhao, P, Wang, Y, Clarke, R & Hoffman, E 2008, 'Motif-directed network component analysis for regulatory network inference', BMC Bioinformatics, vol. 9, no. SUPPL. 1, S21. https://doi.org/10.1186/1471-2105-9-S1-S21
Wang, Chen ; Xuan, Jianhua ; Chen, Li ; Zhao, Po ; Wang, Yue ; Clarke, Robert ; Hoffman, Eric. / Motif-directed network component analysis for regulatory network inference. In: BMC Bioinformatics. 2008 ; Vol. 9, No. SUPPL. 1.
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