BioCPR–A Tool for Correlation Plots

Vidal Fey, Dhanaprakash Jambulingam, Henri Sara, Samuel Heron, Csilla Sipeky and Johanna Schleutker
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Vidal Fey: Faculty of Medicine and Health Technology/BioMediTech, Tampere University, 33520 Tampere, Finland
Dhanaprakash Jambulingam: Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20520 Turku, Finland
Henri Sara: Independent Researcher, 20500 Turku, Finland
Samuel Heron: Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20520 Turku, Finland
Csilla Sipeky: Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20520 Turku, Finland
Johanna Schleutker: Cancer Research Unit and FICAN West Cancer Centre, Institute of Biomedicine, University of Turku and Turku University Hospital, 20520 Turku, Finland

Data, 2021, vol. 6, issue 9, 1-11

Abstract: A gene is a sequence of DNA bases through which genetic information is passed on to the next generation. Most genes encode for proteins that ultimately control cellular function. Understanding the interrelation between genes without the application of statistical methods can be a daunting task. Correlation analysis is a powerful approach to determine the strength of association between two variables (e.g., gene-wise expression). Moreover, it becomes essential to visualize this data to establish patterns and derive insight. The most common method for gene expression visualization is to use correlation heatmaps in which the colors of the plot represent strength of co-expression. In order to address this requirement, we developed a visualization tool called BioCPR: Biological Correlation Plots in R. This tool performs both correlation analysis and subsequent visualization in the form of an interactive heatmap, improving both usability and interpretation of the data. BioCPR is an R Shiny-based application and can be run locally in Rstudio or a web browser.

Keywords: correlation heatmaps; gene expression; r shiny application (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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