Single assay-wide variance experimental (SAVE) design for high-throughput screening.
Bioinformatics
; 29(23): 3067-72, 2013 Dec 01.
Article
em En
| MEDLINE
| ID: mdl-24058057
ABSTRACT
MOTIVATION Advantages of statistical testing of high-throughput screens include P-values, which provide objective benchmarks of compound activity, and false discovery rate estimation. The cost of replication required for statistical testing, however, may often be prohibitive. We introduce the single assay-wide variance experimental (SAVE) design whereby a small replicated subset of an entire screen is used to derive empirical Bayes random error estimates, which are applied to the remaining majority of unreplicated measurements. RESULTS:
The SAVE design is able to generate P-values comparable with those generated with full replication data. It performs almost as well as the random variance model t-test with duplicate data and outperforms the commonly used Z-scores with unreplicated data and the standard t-test. We illustrate the approach with simulated data and with experimental small molecule and small interfering RNA screens. The SAVE design provides substantial performance improvements over unreplicated screens with only slight increases in cost.
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Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
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Preparações Farmacêuticas
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Ensaios de Triagem em Larga Escala
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Modelos Teóricos
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Screening_studies
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article