Unsupervised modeling of cell morphology dynamics for time-lapse microscopy

被引：0

作者：

Zhong, Qing ^{[1
,2
]}

Busetto, Alberto Giovanni ^{[2
,3
]}

Fededa, Juan P. ^{[1
]}

Buhmann, Joachim M. ^{[2
,3
]}

Gerlich, Daniel W. ^{[1
,3
]}

机构：

[1] ETH, Inst Biochem, Zurich, Switzerland

[2] ETH, Dept Comp Sci, Zurich, Switzerland

[3] ETH, Competence Ctr Syst Physiol & Metab Dis, Zurich, Switzerland

来源：

NATURE METHODS | 2012年 / 9卷 / 07期

基金：

瑞士国家科学基金会;

关键词：

SYSTEMS; BINDING;

D O I：

10.1038/NMETH.2046

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：711 / U267

页数：5

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