Abstract
BackgroundIn prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression. Because of co-transcription of genes within an operon, borrowing information from other genes within the same operon can improve the estimation of relative transcript levels; the estimation of relative levels of transcript abundances is one of the most challenging tasks in experimental genomics due to the high noise level in microarray data. Therefore, techniques that can improve such estimations, and moreover are based on sound biological premises, are expected to benefit the field of microarray data analysisResultsIn this paper, we propose a hierarchical Bayesian model, which relies on borrowing information from other genes within the same operon, to improve the estimation of gene expression levels and, hence, the detection of differentially expressed genes. The simulation studies and the analysis of experiential data demonstrated that the proposed method outperformed other techniques that are routinely used to estimate transcript levels and detect differentially expressed genes, including the sample mean and SAM t statistics. The improvement became more significant as the noise level in microarray data increases.ConclusionBy borrowing information about transcriptional activity of genes within classified operons, we improved the estimation of gene expression levels and the detection of differentially expressed genes.
Highlights
In prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression
Simulation study We carried out three simulations, with similar settings and the noise level gradually increasing from simulation 1 to 3
The mean squared errors of the two estimates, the posterior mean from the proposed model and the sample mean, are shown in Table 1 for comparison
Summary
Genes are organized in operons, and the genes within an operon tend to have similar levels of expression. In most of microarray experiments, transcript levels of thousands of genes are measured with a relatively small number of replications, so the estimates of true expression levels from microarray data may be poor, mostly due to a small sample size. To address this problem, several statistical methods have been proposed to borrow information from other genes to improve detection of the differentially expressed ones [1,2,3,4,5,6,7,8,9]. If, based on biological knowledge, we can expect that some genes are more likely to express at similar levels (i.e. co-express), we can improve the inference by using information about the activity of those genes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
