Data

Statistical analysis of drug treated cell morphologies from HCS image data

Monash University
Alvin Ng (Aggregated by) Jagath C. Rajapakse (Aggregated by) James G. Evans (Aggregated by) Paul Matsudaira (Aggregated by) Roy E. Welsch (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/03/5a137325b1943&rft.title=Statistical analysis of drug treated cell morphologies from HCS image data&rft.identifier=https://doi.org/10.4225/03/5a137325b1943&rft.publisher=Monash University&rft.description=We have developed a framework for analyzing image data from High Content Screening (HCS) experiments. The Kolomogorov-Smirnov Statistic is used to identify statistically significant image parameters for use in K-means clustering. Clusters that are underrepresented in drug-treated cell populations can be enriched via normalizing by the control clusters. This general methodology can be applied at different drug treatment conditions to identify interesting clusters. We demonstrate how the resulting clusters of morphologies aid in the understanding of the underlying biology of drug-treated cell populations PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.&rft.creator=Alvin Ng&rft.creator=Jagath C. Rajapakse&rft.creator=James G. Evans&rft.creator=Paul Matsudaira&rft.creator=Roy E. Welsch&rft.creator=Victor Horodincu&rft.date=2017&rft_rights=In Copyright&rft_subject=Bioinformatics -- Congresses&rft_subject=Computational biology -- Congresses&rft_subject=Computer vision in medicine -- Congresses&rft_subject=Computational biology -- Methods -- Congresses&rft_subject=Pattern recognition, automated -- Methods -- Congresses&rft_subject=Cell morphology&rft_subject=Data analysis&rft_subject=High content screening&rft_subject=K-means clustering&rft_subject=2008&rft_subject=conference paper&rft_subject=1959.1/63735&rft_subject=monash:7874&rft_subject=Bioinformatics&rft_subject=Pattern Recognition and Data Mining&rft_subject=Bioinformatics Software&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

view details

In Copyright

Access:

Other

Full description

We have developed a framework for analyzing image data from High Content Screening (HCS) experiments. The Kolomogorov-Smirnov Statistic is used to identify statistically significant image parameters for use in K-means clustering. Clusters that are underrepresented in drug-treated cell populations can be "enriched" via normalizing by the control clusters. This general methodology can be applied at different drug treatment conditions to identify "interesting" clusters. We demonstrate how the resulting clusters of morphologies aid in the understanding of the underlying biology of drug-treated cell populations PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Issued: 2017-11-21

Created: 2017-11-21

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related People

Subjects
1959.1/63735 | 2008 | Bioinformatics | Bioinformatics -- Congresses | Bioinformatics Software | Cell morphology | Computational biology -- Congresses | Computational biology -- Methods -- Congresses | Computer vision in medicine -- Congresses | Data analysis | High content screening | K-means clustering | Pattern Recognition and Data Mining | Pattern recognition, automated -- Methods -- Congresses | conference paper | monash:7874 |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers
Similar data you may be interested in: