Bayesian state-space modeling in gene expression data analysis: An application with biomarker prediction

Background and ObjectiveBayesian State Space models are recent advancement in stochastic modeling which capture the randomness of a hidden background process by scrutinizing the prior knowledge and likelihood of observed data. This article elucidate the scope of Bayesian state space modeling on predicting the future expression values of a longitudinal micro array data. MethodsThe study conveniently makes use of longitudinally collected clinical trial data (GSE30531) from NCBI Gene Expression Omnibus (GEO) data repository. Multiple testing methodology using t-test is used for selecting differentially expressed genes between groups for fitting the model. The parameter values of the predictive model and future expression levels are estimated by drawing samples from the posterior joint distribution using a stochastic Markov Chain Monte Carlo (MCMC) algorithm which relies on Gibbs Sampling. The study also made an attempt to get estimates and its 95% Credible Interval through assumptions of different covariance structures like Variance Components, First order Auto Regressive and Unstructured variance-covariance structure to showcase the flexibility of the algorithm. Results72 Distinct genes with significantly different expression levels where selected for model fitting. Parameter estimates showed almost similar trends under different covariance structure assumption. Cross tabulation of gene frequencies having minimum credible interval under each covariance structure and study group showed a significant P value of 0.02. ConclusionsPresent study reveals that Bayesian state space models can be effectively used to explain and predict a complex data like gene expression data.