Nature Biotechnology 24, 167 - 175 (2006)
Published online: 7 February 2006; | doi:10.1038/nbt1186
Statistical practice in high-throughput screening data analysisNathalie Malo1, 2, James A Hanley2, Sonia Cerquozzi1, Jerry Pelletier3
& Robert Nadon1, 41
McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, Canada, H3A 1A4. 2
McGill University Department of Epidemiology, Biostatistics, and Occupational Health, 1020 Pine Avenue West, Montreal, Quebec, Canada, H3A 1A4. 3
McGill University Department of Biochemistry, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada, H3A 1A4. 4
McGill University Department of Human Genetics, 1205 avenue du Docteur Penfield N5/13, Montreal, Quebec, Canada, H3A 1B1.
Correspondence should be addressed to Robert Nadon robert.nadon@mcgill.ca 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.
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