Abstract
Identification of active compounds in high-throughput screening (HTS) contexts can be substantially improved by applying classical experimental design and statistical inference principles to all phases of HTS studies. The authors present both experimental and simulated data to illustrate how true-positive rates can be maximized without increasing false-positive rates by the following analytical process. First, the use of robust data preprocessing methods reduces unwanted variation by removing row, column, and plate biases. Second, replicate measurements allow estimation of the magnitude of the remaining random error and the use of formal statistical models to benchmark putative hits relative to what is expected by chance. Receiver Operating Characteristic (ROC) analyses revealed superior power for data preprocessed by a trimmed-mean polish method combined with the RVM t-test, particularly for small- to moderate-sized biological hits.
Publication types
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Research Support, Non-U.S. Gov't
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Validation Study
MeSH terms
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Animals
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Cell-Free System / drug effects
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Computer Simulation
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Drug Evaluation, Preclinical / methods
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Drug Evaluation, Preclinical / standards
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Drug Evaluation, Preclinical / statistics & numerical data
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False Positive Reactions
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Fluorescent Antibody Technique / methods
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Fluorescent Antibody Technique / standards
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Fluorescent Antibody Technique / statistics & numerical data
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High-Throughput Screening Assays / methods
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High-Throughput Screening Assays / standards*
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High-Throughput Screening Assays / statistics & numerical data*
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Luciferases, Firefly / analysis
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Luciferases, Firefly / metabolism
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Luciferases, Renilla / analysis
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Luciferases, Renilla / metabolism
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Models, Statistical*
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Protein Biosynthesis / drug effects
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Protein Synthesis Inhibitors / isolation & purification
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Protein Synthesis Inhibitors / pharmacology
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ROC Curve
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Random Allocation
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Research Design*
Substances
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Protein Synthesis Inhibitors
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Luciferases, Renilla
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Luciferases, Firefly