Inference of transcriptional regulatory network by two-stage constrained space factor analysis.
BACKGROUND
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.


BACKGROUND
http://kiefer.stat.ucla.edu/lap2/download/bti656_supplement.pdf.
Yu
Tianwei
T
Department of Statistics, University of California-Los Angeles, Los Angeles, CA 90095-1554, USA.
Li
Ker-Chau
KC
D000465:Algorithms; D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*; D015533:Transcriptional Activation / Q000502:physiology
D020543:Proteome; D014157:Transcription Factors