Document Detail

Comparison of Multivariate Data Analysis Strategies for High-Content Screening.
MedLine Citation:
PMID:  21335595     Owner:  NLM     Status:  Publisher    
High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract 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 data set were monitored. As expected, dimension reduction led 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.
Anne Kümmel; Paul Selzer; Martin Beibel; Hanspeter Gubler; Christian N Parker; Daniela Gabriel
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-2-18
Journal Detail:
Title:  Journal of biomolecular screening : the official journal of the Society for Biomolecular Screening     Volume:  -     ISSN:  1552-454X     ISO Abbreviation:  -     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-2-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9612112     Medline TA:  J Biomol Screen     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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