INsulin Secretion ANalysEr

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A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets.
The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician.
Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface.
The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (


# Install insane from CRAN:

# Or the the development version from GitHub:
# install.packages("remotes")


The Shiny (R package) application insane (INsulin Secretion ANalysEr) provides a web interactive tool to import experiments of insulin secretion using cell lines such as EndoC-βH1.

Excel Template (top)

An Excel template is provided within the app to help users import their experiments in an easy way.

The App (top)

insane provides a user-friendly interface which can handle several projects separately.

Technical Quality-Control (top)

insane performs technical quality-control of the optical density measured in each steps of the experiments:

This technical quality-control step checks:

Statistical analyses (top)

insane performs statistical analyses of the experimental conditions, e.g., one silenced gene (siGENE) compared to an insulin secretion reference (siNTP) in two stimulation conditions (Glc and Glc + A).

Conditions are compared using a linear regression with Date and Operator as covariates (if needed) to control for heterogeneity.

If and when some experiments are failing any of the technical quality-controls, a summary of the issues regarding the selected experiments can be displayed using the button Show Issues in the Selected Experiments.

List of Outliers (Issues Detected) (top)

A comprehensive list of all issues detected in the selected project is available in an Outliers tab.

Note: The Outliers tab is displayed only if there is at least one issue in the selected project.

Getting help

If you encounter a clear bug, please file a minimal reproducible example on github.
For questions and other discussion, please contact the package maintainer.

Please note that this project is released with a Contributor Code of Conduct.
By participating in this project you agree to abide by its terms.