Bioinformatics Advance Access originally published online on September 6, 2005
Bioinformatics 2005 21(21):4033-4038; doi:10.1093/bioinformatics/bti656
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Inference of transcriptional regulatory network by two-stage constrained space factor analysis
Department of Statistics, University of California Los Angeles, CA 90095-1554, USA
*To whom correspondence should be addressed.
Motivation: Microarray gene expression and cross-linking chromatin immunoprecipitation data contain voluminous information that can help the identification of transcriptional regulatory networks at the full genome scale. Such high-throughput data are noisy however. In contrast, from the biomedical literature, we can find many evidenced transcription factor (TF)–target gene binding relationships that have been elucidated at the molecular level. But such sporadically generated knowledge only offers glimpses on limited patches of the network. How to incorporate this valuable knowledge resource to build more reliable network models remains a question.
Results: We present a modified factor analysis approach. Our algorithm starts with the evidenced TF–gene linkages. It iterates between the network configuration estimation step and the connection strength estimation step, using the high-throughput data, till convergence. We report two comprehensive regulatory networks obtained for Saccharomyces cerevisiae, one under the normal growth condition and the other under the environmental stress condition.
Contact: kcli{at}stat.ucla.edu
Supplementary information: http://kiefer.stat.ucla.edu/lap2/download/bti656_supplement.pdf
Received on July 25, 2005; revised on August 24, 2005; accepted on August 30, 2005
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