Kuemmel, Anne, Selzer, Paul, Beibel, Martin, Gubler, Hanspeter, Parker, Christian and Gabriel, Daniela (2011) Comparison of multivariate data analysis strategies for high-content screening. Journal of Biomolecular Screening, 16 (3). pp. 338-347. ISSN 1087-0571

Abstract

High content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell datasets. Before scoring a treatment the complex datasets are processed (e.g. normalized, reduced to a lower dimensionality) to help extracting valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data processing strategies the prediction accuracies and the Z’ factors of control compounds of a HCS cell cycle dataset were monitored. As expected dimension reduction leads to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

Item Type:	Article
Additional Information:	author can archive post-print (ie final draft post-refereeing); Publisher's version/PDF cannot be used
Keywords:	high-content screening, multivariate data analysis, dimension reduction, well summary
Date Deposited:	13 Oct 2015 13:16
Last Modified:	13 Oct 2015 13:16
URI:	https://oak.novartis.com/id/eprint/3239