Calculation
Run the calculation once you have completely set up the assay document. Results of the calculation are added to the assay document.
| Step | Action | Description | Related concept |
|---|---|---|---|
|
1 |
Response data processing |
Use response data processing to configure the processing of response data, for example, to subtract the absorbance of blank wells or to divide response values by the mean absorbance of maximum binding wells. |
|
|
2 |
Outlier detection |
Use outlier detection to detect outlying response values. PLA 3.0 supports replicate-based outlier detection (Dixon, Grubbs, and Standard deviation tests) and model-based outlier detection (Studentized residuals test). How PLA 3.0 handles statistical outliers depends on the outlier processing setting. For example, you can exclude them from further analysis or mark them as outliers. |
|
|
3 |
Regression analysis |
Regression analysis runs two regressions using a full and a restricted model. PLA 3.0 uses the difference between full and restricted models to analyze the suitability of a system in suitability testing. |
|
|
4 |
Analysis of Variance |
Use Analysis of Variance (ANOVA) to determine sources of variation in statistical models. ANOVA employs several methods to estimate variance in the data. It then tests to what extent the results agree. |
|
|
5 |
Potency calculation |
The primary goal of quantitative response assays is to calculate relative potencies of Test samples compared to a Standard sample. You can also calculate absolute potencies if you provide information on stock solutions or raw materials. In this case, assays still calculate relative potencies first and then perform calculations to determine absolute potencies. |
|
|
6 |
Test system |
Assays are based on curve similarity. Suitability tests allow you to check the validity of calculations every time they are performed. |