Incorporating prior information via shrinkage: a combined analysis of genome-wide location data and gene expression data.
Transcriptional control is a critical step in regulation of gene expression. Understanding such a control on a genomic level involves deciphering the mechanisms and structures of regulatory programmes and networks. A difficulty arises due to the weak signal and high noise in various sources of data while most current approaches are limited to analysis of a single source of data. A natural alternative is to improve statistical efficiency and power by a combined analysis of multiple sources of data. Here we propose a shrinkage method to combine genome-wide location data and gene expression data to detect the binding sites or target genes of a transcription factor. Specifically, a prior 'non-target' gene list is generated by analysing the expression data, and then this information is incorporated into the subsequent binding data analysis via a shrinkage method. There is a Bayesian justification for this shrinkage method. Both simulated and real data were used to evaluate the proposed method and compare it with analysing binding data alone. In simulation studies, the proposed method gives higher sensitivity and lower false discovery rate (FDR) in detecting the target genes. In real data example, the proposed method can reduce the estimated FDR and increase the power to detect the previously known target genes of a broad transcription regulator, leucine responsive regulatory protein (Lrp) in Escherichia coli. This method can also be used to incorporate other information, such as gene ontology (GO), to microarray data analysis to detect differentially expressed genes.
Xie
Yang
Y
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Pan
Wei
W
Jeong
Kyeong S
KS
Khodursky
Arkady
A
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000235:genetics; D015870:Gene Expression; D046228:Microarray Analysis; D015233:Models, Statistical; D012372:ROC Curve; D014481:United States