A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data

Biao Xing and Mark van der Laan
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Biao Xing: Division of Biostatistics, School of Public Health, University of California, Berkeley
Mark van der Laan: Division of Biostatistics, School of Public Health, University of California, Berkeley

No 1144, U.C. Berkeley Division of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory network helps us to understand the complex cellular process. In this paper, we describe a comprehensive statistical approach for constructing the transcriptional regulatory network using data of gene expression, promoter sequence, and transcription factor binding sites. Our simulation studies show that the overall and false positive error rates in the estimated transcriptional regulatory network are expected to be small if the systematic noise in the constructed feature matrix is small. Our analysis based on 658 microarray experiments on yeast gene expression programs and 46 transcription factors suggests that the method is capable of identifying important transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.

Keywords: cross-validation model selection; microarray; mixture model; multipletesting; transcriptional regulatory network (search for similar items in EconPapers)
Date: 2004-09-28
Note: oai:bepress.com:ucbbiostat-1144
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