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Journal of Biomolecular Screening, Vol. 12, No. 8, 1042-1049 (2007)
DOI: 10.1177/1087057107309036
© 2007 Society for Biomolecular Sciences

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Oliver Dürr

Genedata AG, Basel, Switzerland, oliver.duerr{at}genedata.com

François Duval

Merck Serono International SA, Geneva, Switzerland

Anthony Nichols

Merck Serono International SA, Geneva, Switzerland

Paul Lang

Merck Serono International SA, Geneva, Switzerland

Annette Brodte

Genedata AG, Basel, Switzerland

Stephan Heyse

Genedata AG, Basel, Switzerland

Dominique Besson

Merck Serono International SA, Geneva, Switzerland

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. (Journal of Biomolecular Screening 2007:1042-1049)

Key Words: phenotypic assay • high-content screening • multivariate data analysis • cellular imaging • systems cell biology • machine learning


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