Exploring the complexity of pathwaydrug relationships using latent Dirichlet allocation
Analysis of cellular responses to diverse stimuli enables the exploration in the complexity of functional genomics. Typically, high-throughput microarray data allow us to identify genes that are differentially expressed under a phenomenon of interest. To extract the meanings from the long list of those differentially expressed genes, we present a new method pathway-based LDA to determine pathways/gene sets that are perturbed after exposure to different chemicals. In this study, a pathway is defined as a group of functionally related genes. Specifically, we have implemented a probabilistic Latent Dirichlet Allocation (LDA) model to learn drugpathwaygene relations by taking known genepathway memberships as prior knowledge. We applied the pathway-based LDA model and 236 known pathways in order to determine pathway responsiveness to gene expression data of 1169 drugs. Our method yielded a better predictive performance on pathway responsiveness to drug treatments than the existing methods. Moreover, the pathway-based LDA also revealed genes contributing the most in each pre-defined pathway through a probabilistic distribution of genes. In achieving that, our method could provide a useful estimator of the pathway complexity of a genome.
functional genomics latent dirichlet allocation (lda) biological pathways genome complexity bayesian inference