title: Motif-directed network component analysis for regulatory network inference.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.forename: Po
authors.initials: P
authors.lastname: Zhao
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Eric
authors.initials: E
authors.lastname: Hoffman
mesh_terms: D001483:Base Sequence; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D008957:Models, Genetic; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics*
chemical_list: D014157:Transcription Factors