ggVolcanoR: A Shiny app for customizable visualization of differential expression datasets

Abstract

Volcano and other analytical plots (e.g., correlation plots, upset plots, and heatmaps) serve as important data visualization methods for transcriptomic and proteomic analyses. Customizable generation of these plots is fundamentally important for a better understanding of dysregulated expression data and is therefore instrumental for the ensuing pathway analysis and biomarker identification. Here, we present an R-based Shiny application, termed ggVolcanoR, to allow for customizable generation and visualization of volcano plots, correlation plots, upset plots, and heatmaps for differential expression datasets, via a user-friendly interactive interface in both local executable version and web-based application without requiring programming expertise. Compared to currently existing packages, ggVolcanoR offers more practical options to optimize the generation of publication-quality volcano and other analytical plots for analyzing and comparing dysregulated genes/proteins across multiple differential expression datasets. In addition, ggVolcanoR provides an option to download the customized list of the filtered dysregulated expression data, which can be directly used as input for downstream pathway analysis. The source code of ggVolcanoR is available at https://github.com/KerryAM-R/ggVolcanoR and the webserver of ggVolcanoR 1.0 has been deployed and is freely available for academic purposes at https://ggvolcanor.erc.monash.edu/.

Keywords: Correlation plot; Data visualization; Differential expression data; Heatmap; Proteomics; Transcriptomics; Upset plot; Volcano plot.

Fig. 1

Using ggVolcanoR to generate volcano plots. (A) Nine panels for data uploading and parameter configuration; (B) an example of the generated volcano plot using the dataset by Goncalves et al.; (C) an example demonstrated seven selected genes of interest in the volcano plot; (D) the ‘Table with links’ tab for plotted dysregulated genes; and (E) the statistical information of different types of genes in the ‘Summary table’ tab.

Fig. 2

Using ggVolcanoR to analyze and compare dysregulated genes and proteins across transcriptomic and proteomic datasets. (A) Eight panels for parameter configuration to generate the correlation plot; (B) the correlation plot generated using the transcriptomic and proteomic datasets by Goncalves et al.; (C) the statistics of the Pearson’s correlation; (D) the ‘Correlation table’ tab representing the overlap of the two datasets; and (E) the bar chart illustrating the agreement of gene/protein dysregulation in the ‘Correlation table’ tab across the transcriptomic and proteomic.

Fig. 3

Using heatmap and upset plot to compare dysregulated genes in both transcriptomic and proteomic datasets. (A) A heatmap by ggVolcanoR demonstrating dysregulated proteins/genes; and (B) an upset plot showing agreement of dysregulated proteins/genes across the transcriptomic and proteomic datasets.