Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102613
Title: Digging deep into Golgi phenotypic diversity with unsupervised machine learning
Authors: Wang, Yi
Huang, Dong
Bard, Frederic
Ke, Yiping
Lee, Kee Khoon
Hussain, Shaista
Le Guezennec, Xavier
Chia, Joanne
Keywords: Golgi
Phenotypic Diversity
DRNTU::Science::Biological sciences
Issue Date: 2017
Source: Hussain, S., Le Guezennec, X., Wang, Y., Huang, D., Chia, J., Ke, Y., . . . Bard, F. (2017). Digging deep into Golgi phenotypic diversity with unsupervised machine learning. Molecular Biology of the Cell, 28(25), 3686-3698. doi: 10.1091/mbc.e17-06-0379
Series/Report no.: Molecular Biology of the Cell
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 stratify phenotypic similarities among genetic perturbations. The derived phenotypic network partially overlaps previously reported protein–protein interactions as well as suggesting novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organizational states and provides a proof of concept for the classification of drugs and genes using fine-grained phenotypic information.
URI: https://hdl.handle.net/10356/102613
http://hdl.handle.net/10220/47249
ISSN: 1059-1524
DOI: 10.1091/mbc.e17-06-0379
Rights: © 2017 Hussain, Le Guezennec, et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
Digging deep into Golgi phenotypic diversity with unsupervised machine learning.pdf5.03 MBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.