Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

Background: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. Results: Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Grace S. Shieh, Chung-Ming Chen, Ching-Yun Yu, Juiling Huang, Woei-Fuh Wang, Yi-Chen Lo
• Keywords: BMCBI 2008, Gene Networks, Latent Factors, Observed Variables