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.
Highlights
Licensee MDPI, Basel, Switzerland.Correlation analysis is a statistical method used to ascertain the strength and direction of the relationship between two variables
BioCPR is an open-source application written in the R programming language [12]
Utilizes the Shiny library, an open-source R package that provides a framework for building interactive applications with a graphical user interface (GUI) that can be run either from
Summary
In a biological context, such as the study of gene expression, the interpretation of correlation coefficients can become a strenuous task due to the number of variables. There are several notable R packages for this purpose, including corrplot [9], ggcorrplot [10], and GGally [11], which visualize correlation in the form of heatmaps These packages require a user to have knowledge of R programming and to calculate the correlation matrix themselves. There is a lack of user-friendly, web-based interactive applications that are published in peer-reviewed journals To fill this gap, and without imposing a great deal of prior data analysis experience on the user, we developed BioCPR. Both significance asterisks and gene highlighting are features that are, to the authors’ knowledge, not readily available in current R packages that produce correlation heatmaps
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