Publication type: Article in scientific journal
Type of review: Not specified
Title: Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay
Authors : Dürr, Oliver
Duval, François
Nichols, Anthony
Lang, Paul
Brodte, Annette
Heyse, Stephan
Besson, Dominique
DOI : 10.1177/1087057107309036
Published in : Journal of Biomolecular Screening
Volume(Issue) : 12
Issue : 8
Pages : 1042
Pages to: 1049
Issue Date: 2007
Publisher / Ed. Institution : Sage
ISSN: 1087-0571
1552-454X
Language : English
Subjects : Cluster analysis; Multivariate analysis; Neurites; Quality control; Reproducibility of results; Tissue array analysis
Subject (DDC) : 572: Biochemistry
Abstract: Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only.
URI: https://digitalcollection.zhaw.ch/handle/11475/13890
Fulltext version : Published version
License (according to publishing contract) : Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in Collections:Publikationen School of Engineering

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