Abstract
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.
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Acknowledgements
The authors thank M. Held for processing image data, D. Scheder and C. Sommer for critical comments on the manuscript and R. Stanyte for user annotations of image data. The Gerlich laboratory has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreements no. 241548 (MitoSys) and no. 258068 (Systems Microscopy), from a European Young Investigator award of the European Science Foundation, from an EMBO Young Investigator Programme fellowship to D.W.G. and from the Swiss National Science Foundation. The Buhmann laboratory has received funding from the SystemsX.ch initiative (LiverX and YeastX projects). J.P.F. was funded by an EMBO long-term fellowship.
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Q.Z. contributed to method design and implementation, experiments, data analysis and manuscript writing. A.G.B. contributed to method design, data analysis and manuscript writing. J.P.F. contributed to experiments and data analysis. J.M.B. contributed to method design and data analysis. D.W.G. contributed to method design, data analysis and manuscript writing.
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Supplementary Figures 1–12 , Supplementary Tables 1–3 and Supplementary Notes 1–3 (PDF 2159 kb)
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TC3 software and data (ZIP 657534 kb)
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Zhong, Q., Busetto, A., Fededa, J. et al. Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nat Methods 9, 711–713 (2012). https://doi.org/10.1038/nmeth.2046
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DOI: https://doi.org/10.1038/nmeth.2046
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