FaDA: A web application for regular laboratory data analyses.

Richard Danger,Sophie Brouard,Yodit Feseha,Quentin Moiteaux,Gérard Ramstein,Estelle Geffard,Yanbin Yin

doi:10.1371/journal.pone.0261083

Abstract

Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.

Highlights

Increasing numbers of web-based data analysis and visualization tools have been developed using the R programming package Shiny [1] and made available to researchers
Two examples are provided to exhibit various possibilities offered by Fast Data Analysis (FaDA) and evidencing that results from FaDA are consistent with previous analyses, with gene expression and flow cytometry data, two major methods used in biology research
Receiver operating characteristics (ROC) curves analysis highlighted individual genes able to discriminate both populations with area under the curve (AUC) above 0.7, such as the AKR1C3 gene, which reached an AUC of 0.796 (Fig 2C)

Summary

Introduction

Increasing numbers of web-based data analysis and visualization tools have been developed using the R programming package Shiny [1] and made available to researchers. Shiny tools are enabling wet-laboratory researchers the ability to take advantage of bioinformatics advancements [2]. While they are free and save the user time in the analytic stages without requiring that the user have extensive computational skills, most of the current online Shiny applications are dedicated to specific objectives or technologies, such as shinyheatmap to generate heatmaps for large datasets [3], shinyCircos to build Circos plots from genomic data [4], iDEP for RNAseq data analysis [5] or shinyGEO to analyze gene expression datasets directly from the Gene.

Methods

Results

Conclusion