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Statistical considerations for high throughput
screening data |
| Xian-Jin XIE, |
| Division of Biostatistics,
Department of Clinical Sciences & Simmons Comprehensive Cancer
Center, The University of Texas Southwestern Medical Center, Dallas,
Texas 75390, USA; |
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Abstract High throughput screening (HTS) is a widely used effective approach in genome-wide association and large scale protein expression studies, drug discovery, and biomedical imaging research. How to accurately identify candidate ‘targets’ or biologically meaningful features with a high degree of confidence has led to extensive statistical research in an effort to minimize both false-positive and false-negative rates. A large body of literature on this topic with in-depth statistical contents is available. We examine currently available statistical methods on HTS and aim to summarize some selected methods into a concise, easy-to-follow introduction for experimental biologists.
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| Keywords
high throughput screen
false-positive rate
false-negative rate
target discovery
predictive modeling
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Issue Date: 01 August 2010
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