A statistical method for constructing transcriptional regulatory networks using gene expression and sequence data.
Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory networks helps us to understand the complex cellular process. In this paper, we describe a statistical approach for constructing transcriptional regulatory networks 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 networks 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 significant transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.
Xing
Biao
B
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA. bxing@stat.berkeley.edu
van der Laan
Mark J
MJ
D019295:Computational Biology / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation; D005809:Genes, Regulator; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011401:Promoter Regions, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014158:Transcription, Genetic