Single assay-wide variance experimental (SAVE) design for high-throughput screening.
BACKGROUND
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.
McGill University and Genome Quebec Innovation Centre, Montreal, Quebec H3A 0G1, Canada, McGill Department of Human Genetics, McGill University, Montreal, Quebec H3A 1B1, Canada, Equipe Criblage pour des Molécules Bio-Actives (CMBA), CEA Grenoble, Grenoble Cedex 09, France, INSERM, U823, Université Joseph Fourier-Grenoble 1 and Institut Albert Bonniot, Grenoble F-38706, France.
Carl
C
Murie
Caroline
C
Barette
Laurence
L
Lafanechère
Robert
R
Nadon
D001499:Bayes Theorem; D003198:Computer Simulation; D057166:High-Throughput Screening Assays / Q000379:methods*; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012107:Research Design
D004364:Pharmaceutical Preparations