Analysis of differential gene expression based on Bayesian estimation of variance

An, Jiyuan, Lai, John, Zeng, Lingzao, & Nelson, Colleen (2016) Analysis of differential gene expression based on Bayesian estimation of variance. Current Bioinformatics, 11(2), pp. 164-168.

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Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression variance due to the high amount of ‘background noise’ that is generated from biological equipment and the lack of biological replicates. Bayesian inference has been widely used in the bioinformatics field. In this work, we reveal that the prior knowledge employed in the Bayesian framework also helps to improve the accuracy of differential gene expression analysis when using a small number of replicates. We have developed a differential analysis tool that uses Bayesian estimation of the variance of gene expression for use with small numbers of biological replicates. Our method is more consistent when compared to the widely used cyber-t tool that successfully introduced the Bayesian framework to differential analysis. We also provide a user-friendly web based Graphic User Interface for biologists to use with microarray and RNAseq data. Bayesian inference can compensate for the instability of variance caused when using a small number of biological replicates by using pseudo replicates as prior knowledge. We also show that our new strategy to select pseudo replicates will improve the performance of the analysis. - See more at:

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ID Code: 89647
Item Type: Journal Article
Refereed: Yes
Keywords: Gene expression, Prostate cancer, Bayesian Estimation of Variance, Bioinformatics
DOI: 10.2174/1574893611666160125221655
ISSN: 2212-392X
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > ONCOLOGY AND CARCINOGENESIS (111200) > Cancer Genetics (111203)
Divisions: Current > Schools > School of Biomedical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Deposited On: 29 Oct 2015 02:53
Last Modified: 21 Aug 2016 21:36

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