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Statistical practice in high-throughput screening data analysis

Statistical practice in high-throughput screening data analysis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Biotechnology Springer Journals

Statistical practice in high-throughput screening data analysis

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References (29)

Publisher
Springer Journals
Copyright
Copyright © 2006 by Nature Publishing Group
Subject
Life Sciences; Life Sciences, general; Biotechnology; Biomedicine, general; Agriculture; Biomedical Engineering/Biotechnology; Bioinformatics
ISSN
1087-0156
eISSN
1546-1696
DOI
10.1038/nbt1186
Publisher site
See Article on Publisher Site

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.

Journal

Nature Biotechnology – Springer Journals

Published: Feb 7, 2006

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