Statistical analysis of drug treated cell morphologies from HCS image data
datasetposted on 21.11.2017, 00:28 authored by Ng, Alvin, Horodincu, Victor, Rajapakse, Jagath C., Welsch, Roy E., Matsudaira, Paul, Evans, James G.
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.
Bioinformatics -- CongressesComputational biology -- CongressesComputer vision in medicine -- CongressesComputational biology -- Methods -- CongressesPattern recognition, automated -- Methods -- CongressesCell morphologyData analysisHigh content screeningK-means clustering2008conference paper1959.1/63735monash:7874Bioinformatics SoftwareBioinformaticsPattern Recognition and Data Mining