2019/03/07 by Oliver M. Crook, Crook, Oliver M., Kathryn S. Lilley +6 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Statistical Methods and Bayesian Inference #stat.AP
paper · pdf · doi:10.48550/arxiv.1903.02909
44 pages, 10 figures, 2 tables
openalex publication_date 2019/03/07 · arxiv created 2019/03/11 · arxiv updated 2019/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding sub-cellular protein localisation is an essential component to analyse context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. Proteins with a priori labelled locations motivate using semi-supervised learning to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. As in other recent work, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied it order to reduce the computational complexity of inversion and hence accelerate computation. A stand-alone R-package implementing these methods using high-performance C++ libraries is available at: https://github.com/ococrook/toeplitz