Online phenotype discovery based on minimum classification error model

Authors:
Zheng Yin;Xiaobo Zhou;Youxian Sun;Stephen T. C. Wong
Affiliations:
State Key Laboratory of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province 310027, China and Center for Biotechnology and Informatics, The Methodist Hos ...;Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA;State Key Laboratory of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province 310027, China;Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.