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Statistical Software for Data Analysis and Clinical Trials

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FaDA: A web application for regular laboratory data analyses.

Richard Danger¹, Quentin Moiteaux^1,2, Yodit Feseha¹

¹Nantes Université, CHU Nantes, Inserm, Centre de Recherche en Transplantation et Immunologie (CRTI), UMR 1064, ITUN, Nantes, France.

|December 20, 2021

Summary

FaDA is a free web application for biologists to analyze common lab data. It offers statistical tests and visualizations, simplifying complex analyses for non-computational scientists.

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Area of Science:

Biotechnology
Bioinformatics
Data Science

Background:

Existing web tools often cater to specialized sequencing data (RNA-Seq, single-cell RNA-Seq).
There is a growing need for accessible, general-purpose tools for common laboratory data analysis.
Advancements in instruments like qPCR, flow cytometry, and ELISA generate large datasets requiring analysis.

Purpose of the Study:

To develop a user-friendly, web-based application for statistical analysis and data visualization of common laboratory data.
To empower biologists without computational expertise to perform routine data analysis.
To provide a versatile tool supporting various wet-laboratory experimental outputs.

Main Methods:

Developed using the R Shiny package for interactive web application creation.
Integrates statistical analysis capabilities including parametric and nonparametric tests with multiple testing corrections.
Incorporates data visualization features such as heatmaps, principal component analysis (PCA) plots, correlograms, and receiver operating characteristic (ROC) curves.

Main Results:

FaDA offers a free, intuitive interface for performing statistical group comparisons.
The application supports multiple testing corrections essential for robust analysis.
Users can generate various plots including heatmaps, PCA, correlograms, and ROC curves for data interpretation.

Conclusions:

FaDA democratizes data analysis for biologists by providing an accessible platform.
The tool facilitates quick and easy analysis of common laboratory data, reducing reliance on bioinformaticians.
FaDA enhances the utility of conventional laboratory instruments by enabling straightforward data interpretation.