A causal inference approach for constructing transcriptional regulatory networks

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.

This paper describes a causal inference approach for constructing transcriptional regulatory networks using gene expression data, promoter sequences and information on transcription factor (TF) binding sites. The method first identifies active TFs in each individual experiment using a feature selection approach. TFs are viewed as "treatments" and gene expression levels as "responses". For every TF and gene pair, a marginal structural model is built to estimate the causal effect of the TF on the expression level of the gene. The model parameters can be estimated using the G-computation 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 TF regulates a gene. These results are further used to infer the overall regulatory network structures.

Our analysis of yeast data suggests that the method is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.

The software is under development.