( common characteristic of cell-based as...)Is Z' factor the best assessment for the quality of cellular assays delivering higher content?

common characteristic of cell-based assays (in contrast to many in vitro assays) is that the statistical variation associated with control and treated sample populations is often significantly different (Figure 1). This phenomenon may be both cell type- and target-dependent. The causes may be manifold, but inherent cell population heterogeneity, particularly with respect to cell cycle postion, and methods of image analysis have been noted as potential contributory factors.

Assay performance assessments based on a signal-to-noise (S:N) method that does not take into account the standard deviation of both control and treated samples will be prone to error. For example, a signal-to-noise metric still cited in screening literature for assessment of in vitro assays involves dividing the magnitude of assay response by the standard deviation of the control (untreated) sample:

We used this S:N calculation to assess assay performance of eight replicate plates imaged and analyzed using the IN Cell Analyzer 1000. The S:N values obtained using this method varied greatly (Figure 2, S:N, method A).

By contrast, S:N values were much more consistent between replicate plates (Figure 2, method B) when we used an alternative method for S:N calculation that takes into account variation of both the control and responding sample populations:

During assay optimization, S:N (method B) can be a more sensitive indicator of assay performance than the Z factor. This is demonstrated by the data shown in Figure 3, where Z factor and S:N values of the same assay performance data are compared.

Since both S:N (method B) and Z factor are based on the same variables, there is a defined relationship between the two metrics, assuming for simplicity that standard deviation of the positive
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