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Nat Methods. 2012 May 27;9(7):711-3. doi: 10.1038/nmeth.2046.

Unsupervised modeling of cell morphology dynamics for time-lapse microscopy.

Author information

1
Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.

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.

PMID:
22635062
DOI:
10.1038/nmeth.2046
[Indexed for MEDLINE]

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