Semi-supervised Bayesian integration of multiple spatial proteomics datasets.

ABSTRACT: The subcellular localisation of proteins is a key determinant of their function. High-throughput analyses of these localisations can be performed using mass spectrometry-based spatial proteomics, which enables us to examine the localisation and relocalisation of proteins. Furthermore, complementary data sources can provide additional sources of functional or localisation information. Examples include protein annotations and other high-throughput 'omic assays. Integrating these modalities can provide new insights as well as additional confidence in results, but existing approaches for integrative analyses of spatial proteomics datasets, such as concatenation-based methods and transfer learning approaches like KNN-TL, are limited in the types of data they can integrate and do not quantify un

SUBMITTER: Coleman S 

PROVIDER: S-EPMC12721539 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature