Motif-directed network component analysis for regulatory network inference

Background: Network Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIPon-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 ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Chen Wang, Jianhua Xuan, Li Chen, Po Zhao, Yue Wang, Robert Clarke, Eric P. Hoffman
• Keywords: BMCBI 2008, Gene Expression Data, Microarray Data, Regulatory Network