Digging deep into Golgi phenotypic diversity with unsupervised machine learning
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 th...
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/102613 http://hdl.handle.net/10220/47249 |
| _version_ | 1811684021864759296 |
|---|---|
| author | Wang, Yi Huang, Dong Bard, Frederic Ke, Yiping Lee, Kee Khoon Hussain, Shaista Le Guezennec, Xavier Chia, Joanne |
| author2 | Sommer, Thomas |
| author_facet | Sommer, Thomas Wang, Yi Huang, Dong Bard, Frederic Ke, Yiping Lee, Kee Khoon Hussain, Shaista Le Guezennec, Xavier Chia, Joanne |
| author_sort | Wang, Yi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:22:01Z |
| format | Journal Article |
| id | ntu-10356/102613 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:22:01Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/1026132020-03-07T11:50:48Z Digging deep into Golgi phenotypic diversity with unsupervised machine learning Wang, Yi Huang, Dong Bard, Frederic Ke, Yiping Lee, Kee Khoon Hussain, Shaista Le Guezennec, Xavier Chia, Joanne Sommer, Thomas School of Computer Science and Engineering Golgi Phenotypic Diversity DRNTU::Science::Biological sciences 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. Published version 2018-12-27T06:36:03Z 2019-12-06T20:57:35Z 2018-12-27T06:36:03Z 2019-12-06T20:57:35Z 2017 Journal Article 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 1059-1524 https://hdl.handle.net/10356/102613 http://hdl.handle.net/10220/47249 10.1091/mbc.e17-06-0379 en Molecular Biology of the Cell © 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). 13 p. application/pdf |
| spellingShingle | Golgi Phenotypic Diversity DRNTU::Science::Biological sciences Wang, Yi Huang, Dong Bard, Frederic Ke, Yiping Lee, Kee Khoon Hussain, Shaista Le Guezennec, Xavier Chia, Joanne Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title | Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title_full | Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title_fullStr | Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title_full_unstemmed | Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title_short | Digging deep into Golgi phenotypic diversity with unsupervised machine learning |
| title_sort | digging deep into golgi phenotypic diversity with unsupervised machine learning |
| topic | Golgi Phenotypic Diversity DRNTU::Science::Biological sciences |
| url | https://hdl.handle.net/10356/102613 http://hdl.handle.net/10220/47249 |
| work_keys_str_mv | AT wangyi diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT huangdong diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT bardfrederic diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT keyiping diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT leekeekhoon diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT hussainshaista diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT leguezennecxavier diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning AT chiajoanne diggingdeepintogolgiphenotypicdiversitywithunsupervisedmachinelearning |