Document Type
Article
Publication Date
2019
Department
Mathematics, Statistics, and Computer Science
Keywords
Bayesian, bacteria, genetics
Abstract
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 cell's 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 Stan's 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.
Source Publication Title
Pacific Symposium on Biocomputing
Publisher
World Scientific Publishing Company
Volume
24
First Page
172
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Li, K., Chen, R., Lindsey, W., Best, A., DeJongh, M., Henry, C., & Tintle, N. L. (2019). Implementing and Evaluating a Gaussian Mixture Framework for Identifying Gene Function from TnSeq Data. Pacific Symposium on Biocomputing, 24, 172. Retrieved from https://digitalcollections.dordt.edu/faculty_work/1257
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