Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.

ABSTRACT: Understanding sub-cellular protein localisation is an essential component in the analysis of 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 K-component 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. The availability of marker proteins (i.e. proteins wit

SUBMITTER: Crook OM 

PROVIDER: S-EPMC7613899 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature