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Bioinformatics Advance Access published online on August 30, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti648
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 27, 2005
Revised August 6, 2005
Accepted August 25, 2005

Article

A causal inference approach for constructing transcriptional regulatory networks

Biao Xing 1* and Mark J. van der Laan 2

1 Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA
2 Division of Biostatistics, School of Public Health, University of California, 140 Warren Hall #7360, Berkeley, CA 94720, USA

* To whom correspondence should be addressed.
Biao Xing, E-mail: xing.biao{at}gene.com


  Abstract

Motivation: 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.

Method: This paper describes a causal inference approach for constructing transcriptional regulatory networks using gene expression data, promoter sequences and information on transcription factor binding sites. The method first identifies active transcription factors under each individual experiment using a feature selection approach. 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 effect of the transcription factor 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 transcription factor regulates a gene. These results are further used to infer the overall regulatory network structures.

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

Availability: The software is under development.


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