Implementing and Evaluating a Gaussian Mixture Framework for Identifying Gene Function from Tnseq Data Conference: Conference on Constraint-Based Reconstruction and Analysis (COBRA)Year: 2018Proceeding: 5th Conference on Constraint-Based Reconstruction and Analysis (COBRA 2018)Group: Poster SessionSession: Poster Session Time: Sunday, October 14, 2018 - 6:00pm-7:00pm Authors: Li, K., Columbia University Chen, R., North Carolina State University Lindsey, W., Dordt College Best, A., Hope College DeJongh, M., Hope College Henry, C. S., Argonne National Laboratory Tintle, N., Dordt College The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cellâs fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stanâs Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.