Unsupervised clustering algorithms for flow/mass cytometry data
This Final Year Project report documents the process of using dimension reduction and unsupervised clustering methods for clustering similar group of cells and to automate the discovery of cell populations from data sets generated from mass cytometry. Also, it documents the process of developing a w...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2015
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| Online Access: | http://hdl.handle.net/10356/65605 |
| _version_ | 1811693755762212864 |
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| author | Koh, Kavan Li Wenn |
| author2 | Chen Jinmiao |
| author_facet | Chen Jinmiao Koh, Kavan Li Wenn |
| author_sort | Koh, Kavan Li Wenn |
| collection | NTU |
| description | This Final Year Project report documents the process of using dimension reduction and unsupervised clustering methods for clustering similar group of cells and to automate the discovery of cell populations from data sets generated from mass cytometry. Also, it documents the process of developing a website to display the details of mass cytometry datasets. Traditionally, flow cytometry is used to analyse physical and chemical properties of cells by flowing a stream of fluid containing the cells through a detection device. Mass cytometry uses antibodies and rare earth elements to tag the cells which are then analysed by the mass spectrometer based on the time-of flight of these cells. |
| first_indexed | 2024-10-01T06:56:44Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/65605 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:56:44Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/656052023-03-03T20:47:21Z Unsupervised clustering algorithms for flow/mass cytometry data Koh, Kavan Li Wenn Chen Jinmiao Lin Feng School of Computer Engineering A*STAR Singapore Immunology Network (SIgN) DRNTU::Engineering::Computer science and engineering This Final Year Project report documents the process of using dimension reduction and unsupervised clustering methods for clustering similar group of cells and to automate the discovery of cell populations from data sets generated from mass cytometry. Also, it documents the process of developing a website to display the details of mass cytometry datasets. Traditionally, flow cytometry is used to analyse physical and chemical properties of cells by flowing a stream of fluid containing the cells through a detection device. Mass cytometry uses antibodies and rare earth elements to tag the cells which are then analysed by the mass spectrometer based on the time-of flight of these cells. Bachelor of Engineering (Computer Engineering) 2015-11-19T05:10:10Z 2015-11-19T05:10:10Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/65605 en Nanyang Technological University 94 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Koh, Kavan Li Wenn Unsupervised clustering algorithms for flow/mass cytometry data |
| title | Unsupervised clustering algorithms for flow/mass cytometry data |
| title_full | Unsupervised clustering algorithms for flow/mass cytometry data |
| title_fullStr | Unsupervised clustering algorithms for flow/mass cytometry data |
| title_full_unstemmed | Unsupervised clustering algorithms for flow/mass cytometry data |
| title_short | Unsupervised clustering algorithms for flow/mass cytometry data |
| title_sort | unsupervised clustering algorithms for flow mass cytometry data |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/65605 |
| work_keys_str_mv | AT kohkavanliwenn unsupervisedclusteringalgorithmsforflowmasscytometrydata |