Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their target genes. Discovering transcriptional regulatory networks helps us to understand the underlying mechanism of complex cellular processes and responses. In this paper, we describe a causal inference approach for constructing transcriptional regulatory networks using gene expression data, promoter sequences and information on transcription factor binding sites. The method rst identies active transcription factors under each individual experiment using a feature selection approach similar to Bussemaker et al. (2001), Keles et al. (2002) and Conlon et al. (2003). Transcription factors are viewed as `treatments' and gene expression levels as `responses'. For every transcription factor and gene pair, a marginal structural model is built to estimate the causal eect of the transcription factor on the expression level of the gene. The model parameters can be estimated using either the G-estimation procedure or the IPTW estimator. The p-value associated with the causal parameter in each of these models is used to measure how strongly a transcription factor regulates a gene. These results are further used to infer the overall regulatory network structures. We carried out simulations to assess the performance of our method in the estimation of a ctitious regulatory network. Our analysis of yeast data suggests that the method is capable of identifying signicant transcriptional regulatory interactions and the corresponding regulatory networks.
Xing, Biao and van der Laan, Mark J., "A Causal Inference Approach for Constructing Transcriptional Regulatory Networks" (March 2005). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 169.