View source: R/qualitiyMetrics.R
calculateZPrime | R Documentation |
Calculate Z'-factor of assay quality
calculateZPrime(res, internal = TRUE, nConc = 2)
res |
Object of class MALDIassay |
internal |
Logical, currently only the internal implementation,
using |
nConc |
Numeric, number of top and bottom concentrations to be used
to calculate the pseudo positive and negative control.
Only used if |
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 |
Numeric vector of Z'-factors.
# see example for `fitCurve()` to see how this data was generated
data(Blank2022res)
calculateZPrime(Blank2022res, nConc = 2)
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