Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay.
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.
Dürr
Oliver
O
Genedata AG, Basel, Switzerland. oliver.duerr@genedata.com
Duval
François
F
Nichols
Anthony
A
Lang
Paul
P
Brodte
Annette
A
Heyse
Stephan
S
Besson
Dominique
D
D016000:Cluster Analysis; D015999:Multivariate Analysis; D016501:Neurites / Q000378:metabolism*; D011786:Quality Control; D015203:Reproducibility of Results; D046888:Tissue Array Analysis / Q000592:standards*