Statistical practice in high-throughput screening data analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-02

AUTHORS

Nathalie Malo, James A Hanley, Sonia Cerquozzi, Jerry Pelletier, Robert Nadon

ABSTRACT

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process. More... »

PAGES

167-175

References to SciGraph publications

Journal

TITLE

Nature Biotechnology

ISSUE

2

VOLUME

24

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nbt1186

DOI

http://dx.doi.org/10.1038/nbt1186

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023338227

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16465162


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