  Sequencing technologies have revolutionised the field of molecular biology.
We now have the ability to routinely capture the complete RNA profile in tissue
samples. This wealth of data allows for comparative analyses of RNA levels at
different times, shedding light on the dynamics of developmental processes, and
under different environmental responses, providing insights into gene
expression regulation and stress responses. However, given the inherent
variability of the data stemming from biological and technological sources,
quantifying changes in gene expression proves to be a statistical challenge.
Here, we present a closed-form Bayesian solution to this problem. Our approach
is tailored to the differential gene expression analysis of processed RNA-Seq
data. The framework unifies and streamlines an otherwise complex analysis,
typically involving parameter estimations and multiple statistical tests, into
a concise mathematical equation for the calculation of Bayes factors. Using
conjugate priors we can solve the equations analytically. For each gene, we
calculate a Bayes factor, which can be used for ranking genes according to the
statistical evidence for the gene's expression change given RNA-Seq data. The
presented closed-form solution is derived under minimal assumptions and may be
applied to a variety of other 2-sample problems.
