Digging deep into Golgi phenotypic diversity with unsupervised machine learning.

ABSTRACT: The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter or supervised analysis with predefined Golgi structural classes. Here, we report the use of multiparametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the three visually definable phenotypes, our framework reproducibly identified 10 Golgi phenotypes. They were used to quantify and st

SUBMITTER: Hussain S 

PROVIDER: S-EPMC5706995 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature