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Is Z' factor the best assessment for the quality of cellular assays delivering higher content?

Elizabeth P. Roquemore, Sam Murphy, *Stephen J. Capper, Suzanne M. Hancock, Elaine Adie, Molly Price-Jones, Stephen Game & Stuart Swinburne
Amersham Biosciences UK Limited, Amersham Place, Little Chalfont, Buckinghamshire HP7 9NA England


Abstract
As high throughput imaging systems, analysis algorithms and associated cellular assays have begun to be used in profiling and secondary analysis, scientists have used tools derived from primary screening such as Z factor to assess assay quality. Image-based cellular assays may be better suited to lead profiling, where the needs are different and modified criteria should be applied in assessing assay quality. We ask the question what statistical metrics most accurately reflect the nature and use of cell-based assays in lead profiling? We present data from a variety of such assays we have developed, such as GFP translocation assays, including simple Z factor analysis and propose multiple statistical approaches to assess assay quality that may be better suited to high information content assays.


Introduction
We have developed a number of live-cell translocation assays that are compatible with high-throughput micro-imaging platforms such as the IN Cell Analyzer 3000 and the IN Cell Analyzer 1000. To assess assay quality during assay development and for subsequent QA, we initially used the well known measure of assay performance, Z factor. While Z factor is of value in assessing the size of the reading window for a screening assay, we observed that in some cases Z factor analysis obscured meaningful differences in assay performance. Consequently, we have explored the use of signal-to-noise as a statistical metric more appropriate to the needs of image-based cellular assays.


Results
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