JSM 2004 - Toronto

Abstract #301846

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Activity Number: 223
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301846
Title: Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data
Author(s): Biao Xing*+ and Mark van der Laan
Companies: University of California, Berkeley and University of California, Berkeley
Address: Division of Biostatistics, School of Public Health, Berkeley, CA, 94720,
Keywords: transcriptional regulatory network ; analysis of microarray data ; transcription factor ; model selection ; false discovery rate ; mixture model
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. We describe a comprehensive statistical approach for constructing the transcriptional regulatory network using data on gene expression, promoter sequences, 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 yeast transcription factors suggests that the method is capable of identifying important transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.

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