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.
Keywords: Microarray, RNAseq, Bayesian inference, variance, likelihood and gene expression.
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