Calculate Z'-factor of assay quality

Usage

calculateZPrime(res, internal = TRUE, nConc = 2)

Arguments

res: Object of class MALDIassay
internal: Logical, currently only the internal implementation, using nConc top and bottom concentrations, is implemented.
nConc: Numeric, number of top and bottom concentrations to be used to calculate the pseudo positive and negative control. Only used if internal is TRUE

Value

Numeric vector of Z'-factors.

Details

The most common way to measure the quality of an assay is the so-called Z'-factor, which describes the separation of the positive and negative control in terms of their standard deviations $\sigma_p$ and $\sigma_n$. The Z'-factor is defined as Ji-Hu Zhang et al., A simple statistical parameter for use in evaluation and validation of high throughput screening assays. $$Z' = 1 - (3 * (\sigma_p+\sigma_n))/|\mu_p-\mu_n|$$

where $\mu_p$ and $\mu_p$ is the mean value of the positive (response expected) and negative (no response expected) control, respectively. Therefore, the assay quality is independent of the shape of the concentration response curve and solely depend on two control values.

Note, if internal is set to TRUE, the nConc highest concentrations are assumed as positive control, whereas the nConc lowest concentrations are used as negative.

Value	Interpretation
Z' ~ 1	perfect assay
1 > Z' > 0.5	excellent assay
0.5 > Z' > 0	moderate assay
Z' = 0	good only for yes/no response
Z' < 0	unacceptable

Examples

# see example for `fitCurve()` to see how this data was generated
data(Blank2022res)
calculateZPrime(Blank2022res, nConc = 2)       
#>  [1] -3.237607219 -1.252868716 -5.541275313 -4.970064620 -1.631465147
#>  [6] -0.957522437 -0.223832609 -0.902466878 -1.017670816 -1.058958794
#> [11] -1.562843832 -2.115946846 -0.024041256 -0.812729462 -0.691440743
#> [16] -0.009526589  0.030512012  0.077456813  0.266325098  0.216154352
#> [21]  0.151846342  0.140734203 -0.020104856