SHADE is a Shiny application for providing support to researchers in design of experiments. It includes a tool to perform power analysis for usual test statistics, as well as a reporter tool to draft the ethics committee form for animal experimentation (CETEA), in a user-friendly interface. Initially created to design experiments using animals, SHADE might be used to design any experiments (cell culture ...).
12/01/2026
SHADE version 4.0 offers a new user interface to facilitate navigation. You can now run a quick power analysis, or choose the guided section to get a detailed analysis according to your experimental design.
01/03/2023
User interface improved with an advanced tab for exploring parameters and language option in final report. Cage effect visualisation was also improved.
01/02/2021
User interface was improved with validation buttons, tabs for exploring parameters. Bugs were fixed such as external data loading and numerical errors in power analysis.
01/01/2019
Implementing power analysis for frequently used statistical tests, power graph and a report for CETEA form.
Choose the appropriate statistical test for your data.
Select a one-sided test if you already know in which way your effect is going to impact the measured variable (increase only or decrease only). If you have no prior knowledge on the effect you are trying to measure, a two-sided test is more suitable.
Will the individuals of the same cage receive the same treatment (nested) of different treatments (crossed) ?
Unwanted sources of variation, such as technical factors, can impact the measures during the experiment. It is possible to take them into account during the power analysis, to have a more accurate estimation.
General parameters
Select pathway
Compute power analysis
Download report
This section is not finished yet : do not hesitate to contact us if you encounter any bugs, or if you have some suggestions ! You can use our mail alias : shiny-stats@pasteur.fr
Unwanted sources of variation can impact the measures during the experiment. Those arre usually technical factors such as cage, repetition of the experiment, batches ... Taking them into account during the power analysis helps to have a more accurate estimation.
Will all the measured be completely independent (unpaired) or are you are following the same individuals through time (paired) ?.
Select a one-sided test if you already know in which way your effect is going to impact the measured variable (increase only or decrease only). If you have no prior knowledge on the effect you are trying to measure, a two-sided test is more suitable.
General parameters
Select pathway
Compute power analysis
Download report
This section is not finished yet : do not hesitate to contact us if you encounter any bugs, or if you have some suggestions ! You can use our mail alias : shiny-stats@pasteur.fr
Depending on the information and data at your disposition, choose the option that fits best :
General parameters
Select pathway
Compute power analysis
Download report
This section is not finished yet : do not hesitate to contact us if you encounter any bugs, or if you have some suggestions ! You can use our mail alias : shiny-stats@pasteur.fr
In order to compute the power analysis, we need a few additional informations :
double-click to edit cell
General parameters
Select pathway
Compute power analysis
Download report
This section is not finished yet : do not hesitate to contact us if you encounter any bugs, or if you have some suggestions ! You can use our mail alias : shiny-stats@pasteur.fr
In order to compute the power analysis, we need a few additional informations :
Enter below the factors of your experiemnt that might introduce biases in your data. All selected unwanted effects will be regrouped to be modelized as "units". On average, a unit effect can represent between 10% and 20% of the total variability.
Those values are conventionnaly set at 5% for false positive rate and 80% for power, feel free to modify them according to the specificities of your field :
General parameters
Select pathway
Compute power analysis
Download report
This section is not finished yet : do not hesitate to contact us if you encounter any bugs, or if you have some suggestions ! You can use our mail alias : shiny-stats@pasteur.fr
Type I error
Power
Effect size
n per group
Below is a summarised report you can copy/paste to the DAP document, to be reviewed by statisticians. You can also download a summary of the analysis to keep track of what parameters were used.
SHADE is an application developed by members of the Hub of Bioinformatics and Biostatistics of the Institut Pasteur.
It provides support to Pasteur research units in Bioinformatics and Biostatistics. The list of the services offered by the Hub are detailed hereafter:
You may find more information about the Hub of Bioinformatics and Biostatistics on the team webpage .
Click here to contact the SHADE authors by email.
We developped other Shiny apps to help you with experimental design. Do not hesitate to have a look !