# insane: INsulin Secretion ANalysEr

#### Mickaël Canouil, Ph.D.

#### November 14, 2023

# 1 Installation

    # Install insane from CRAN:
    install.packages("insane")

    # Or the the development version from GitHub:
    # install.packages("remotes")
    remotes::install_github("mcanouil/insane")

    library("insane")
    go_insane()

# 2 Overview

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.

## 2.1 Excel Template

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

## 2.2 The App

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

### 2.2.1 Technical Quality-Control

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

- blank (BLANK),
- lysat (LYSATE),
- supernatant (SUPERNATANT1 and SUPERNATANT2).

This technical quality-control step checks:

- the variability among the duplicated optical density measures of each samples;
- the variability in the blank curves (intercept and slope estimates) among all experiments in a project.

### 2.2.2 Statistical analyses

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.

- Using all experiments in the selected project

  - Boxplot version

  - Histogram version

- Using some of the experiments in the selected project

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`.

### 2.2.3 List of Outliers (Issues Detected)

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.