You must upload a single CSV file. Your dataset must be seperated in three columns, in this order: (1) log₁₀ Dose, (2) Normalized % responses, and (3) experiments IDs.
The IDs are specific to each DR experiments and should be specified even if you are analyzing a single experiment. The IDs are used to seperate your datasets by eperiments and to identify them in the various graphical representations returned.
Here an example of a dataset that comprises 10 DR experiments with descending responses.
Analysis results are automatically downloaded (.zip file) once the process is complete. Different figures are returned, depending on the number of experiments considered, as well as the complete posteriors values (.csv files) for each efficiency metric of every experiment.
The returned figures are presented bellow.
Ascending response: no response at small concentration and high response at large concentrations.(e.g. % of cell growth inhibition)
Descending response: high response at small concentration and no response at small concentrations (e.g. % of cell survival)
We used the four-paramter log-listic model to evaluate the dose-response relationship. The following four efficiency metrics are inferred and returned:
1. LDR (Low-Dose Response): basal asymptotic response. The response type defines if this is the minimal or maximal response.
2. HDR (High-Dose Response): asymptotic response in the pressence of high dose. The response type defines if this is the minimal or maximal response.
3. IC₅₀: DR curve inflexion point or the log₁₀ dose needed to generate a response that is half of the HDR.
4. Slope: DR curve slope around the IC₅₀
We also infer a σ value that aims to quantify the variability of the experimental responses.
Other efficiency metrics (e.g. AAC/AUC, DSS) can be derived from the returned posteriors.
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