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, Guanghua Xiao Division of Biostatistics, Department of Clinical Sciences, University of Texas, Southwestern Medical Center , Dallas, Texas 75390 , U.S.A. email:guanghua.xiao@utsouthwestern.edu Search for other works by this author on: Oxford Academic Cavan Reilly Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis, Minnesota 55455 , U.S.A. Search for other works by this author on: Oxford Academic Arkady B. Khodursky Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota , St. Paul, Minnesota 55414 , U.S.A. Search for other works by this author on: Oxford Academic
Biometrics, Volume 65, Issue 3, September 2009, Pages 805–814, https://doi.org/10.1111/j.1541-0420.2008.01161.x
Published:
14 September 2009
Article history
Received:
01 February 2008
Revision received:
01 June 2008
Accepted:
01 June 2008
Published:
14 September 2009
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Guanghua Xiao, Cavan Reilly, Arkady B. Khodursky, Improved Detection of Differentially Expressed Genes Through Incorporation of Gene Locations, Biometrics, Volume 65, Issue 3, September 2009, Pages 805–814, https://doi.org/10.1111/j.1541-0420.2008.01161.x
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Summary
In determining differential expression in cDNA microarray experiments, the expression level of an individual gene is usually assumed to be independent of the expression levels of other genes, but many recent studies have shown that a gene's expression level tends to be similar to that of its neighbors on a chromosome, and differentially expressed (DE) genes are likely to form clusters of similar transcriptional activity along the chromosome. When modeled as a one-dimensional spatial series, the expression level of genes on the same chromosome frequently exhibit significant spatial correlation, reflecting spatial patterns in transcription. By modeling these spatial correlations, we can obtain improved estimates of transcript levels. Here, we demonstrate the existence of spatial correlations in transcriptional activity in the Escherichia coli (E. coli) chromosome across more than 50 experimental conditions. Based on this finding, we propose a hierarchical Bayesian model that borrows information from neighboring genes to improve the estimation of the expression level of a given gene and hence the detection of DE genes. Furthermore, we extend the model to account for the circular structure of E. coli chromosome and the intergenetic distance between gene neighbors. The simulation studies and analysis of real data examples in E. coli and yeast Saccharomyces cerevisiae show that the proposed method outperforms the commonly used significant analysis of microarray (SAM) t-statistic in detecting DE genes.
Autocorrelation function, Gene expression, Spatial smoothing
© 2009, The International Biometric Society
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Issue Section:
Biometric Methodology
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