Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
Nat Methods. 2012 May 27;9(7):711-3. doi: 10.1038/nmeth.2046.
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.
在大型成像数据集的细胞表型分析中，传统上涉及需要用户注释的训练数据的监督统计方法。本文介绍了一种基于时间约束组合聚类的无监督学习方法，用于对时分辨图像中的细胞形态类进行自动预测。我们将无监督方法应用于各种荧光标记和筛选数据，并验证了人类细胞表型的准确分类，展示了基于图像的系统生物学中完全客观的数据标记。
