Unsupervised modeling of cell morphology dynamics for time-lapse microscopy.
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
Qing
Q
Zhong
Alberto Giovanni
AG
Busetto
Juan P
JP
Fededa
Joachim M
JM
Buhmann
Daniel W
DW
Gerlich
D002455:Cell Division / Q000502:physiology*; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D034622:RNA Interference; D059008:Time-Lapse Imaging / Q000295:instrumentation / Q000379:methods*