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Single assay-wide variance experimental (SAVE) design for high-throughput screening.

Carl Murie¹, Caroline Barette, Laurence Lafanechère

¹McGill 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.

Bioinformatics (Oxford, England)

|September 24, 2013

Summary

The single assay-wide variance experimental (SAVE) design reduces costs for high-throughput screening by using a small replicated subset to estimate errors for unreplicated data. This method yields reliable P-values comparable to full replication, improving efficiency in drug discovery.

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Area of Science:

Biochemistry
Genomics
Pharmacology

Background:

Statistical testing in high-throughput screening (HTS) offers objective compound activity benchmarks (P-values) and false discovery rate estimation.
The high cost of replication in HTS can be a significant barrier to statistical analysis.
Existing methods for unreplicated data, like Z-scores, lack the statistical rigor of replicated approaches.

Purpose of the Study:

To introduce a cost-effective experimental design for statistical testing in HTS.
To enable robust statistical analysis of HTS data without prohibitive replication costs.
To improve the reliability and performance of HTS data analysis.

Main Methods:

Introduction of the single assay-wide variance experimental (SAVE) design.
Utilizing a small, replicated subset of an entire screen to derive empirical Bayes random error estimates.
Applying these estimates to the majority of unreplicated measurements within the screen.

Main Results:

The SAVE design generates P-values comparable to those from full replication.
SAVE design performance is nearly equivalent to the random variance model t-test with duplicate data.
SAVE design outperforms commonly used Z-scores for unreplicated data and the standard t-test.

Conclusions:

The SAVE design offers substantial performance improvements over unreplicated screens.
This approach achieves significant gains with only a slight increase in experimental cost.
SAVE design is illustrated with simulated data and experimental screens (small molecule, siRNA).