Poster Presentation 27th Annual Lorne Proteomics Symposium 2022

Using ggVolcanoR to visualize differential expression datasets (#156)

Kerry A Mullan 1 , Liesl M Bramberger 1 , Prithvi Raj Munday 1 , Gabriel Goncalves 1 , Jerico Revote 2 , Nicole A Mifsud 1 , Patricia T Illing 1 , Alison Anderson 3 , Patrick Kwan 3 4 5 , Chen Li 1
  1. Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash Univeristy, Clayton, VIC, Australia
  2. Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
  3. Department of Neuroscience, Central Clinical School,, Monash University, Melbourne, VIC, Australia
  4. Departments of Medicine and Neurology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
  5. Department of Neurology, Alfred Health, Melbourne, Victoria, Australia

Advances in transcriptomics and quantitative proteomics have allowed for systematic investigation to better understand differential gene expression. The resulting differential expression data is often visualised in volcano plots, correlation plots, upset plots, and heatmaps. Being able to fully customise these plots is important to interrogate and visualise the data, thereby enabling the identification of biomarkers and dysregulated pathways. To this end, we showcase our recently published Shiny R-based application, ‘ggVolcanoR’(1), a toolkit and webserver designed for non-coders, for the generation of customisable volcano plots, correlation plots, upset plots, and heatmaps using transcriptomic or proteomic expression datasets. Compared to existing applications, ggVolcanoR has vastly more options for generating publication-quality figures, such as customising points of interest by colour, size, shape and transparency, font selection and customisable file size that can be exported as either a png or PDF. Additionally, unlike previous applications, ggVolcanoR is not restricted to a certain differential expression pipeline as it only requires ID, p-value (Pvalue) and log fold change (logFC) for data processing and visualization. Lastly, ggVolcanoR offers an option to download the filtered list of genes, which can then be used for downstream pathway analysis. The source code of ggVolcanoR is available at https://github.com/KerryAM-R/ggVolcanoR and the fully functional webserver of ggVolcanoR 1.0 has been deployed and is freely available for academic purposes at https://ggvolcanor.erc.monash.edu/.

 

  1. 1. Mullan KA, Bramberger LM, Munday PR, Goncalves G, Revote J, Mifsud NA, et al. ggVolcanoR: A Shiny app for customizable visualization of differential expression datasets. Comput Struct Biotechnol J. 2021;19:5735-40.