DEVisComp

Description

DEVisComp is an R package for visualizing and comparing the differential expression analysis results from R packages DESeq2 and edgeR. This package provide different plots such as MA plots and Venn diagrams comparing the differntially expressed genes data generated by DESeq2 and edgeR. It can also be used to visualize the pattern of gene expression together with log-fold change and adjusted p-values. In order for the result to be meaningful, the package assumes the results from DESeq2 and edgeR should be created with similar parameters.

The package is developed under R 4.1.1 in Mac.

Installation

To install the latest version of the package:

require("devtools")
devtools::install_github("Lori-tan/DEVisComp", build_vignettes = TRUE)
library("DEVisComp")

To run the Shiny app:

runDEVisComp()

Overview

ls("package:DEVisComp")
data(package = "DEVisComp") # optional

DEVisComp contains 1 functions to visualize the expression analysis results and 3 functions to compare the results created by DESeq2 and edgeR. The ClusterTogether function generates 3 heatmaps side-by-side showing the patterns among gene expression, log-fold changes, and adjusted p-values of the differntially expressed genes. The compVenn is the function that create a Venn diagram comparing the genes that are marked as differentially expressed by DESeq2 and edgeR. The compMA and compVolcano generate MA plots and Volcano plots respectively for results created by DESeq2 and edgeR.

browseVignettes("DEVisComp")

An overview of the package is illustrated below.

Contributions

The author of the package is Luomeng Tan. The ClusterTogether function makes use of pheatmap function from pheatmap R package to plot the heatmaps. The stats R package is used for calculating the standard deviation between counts in different condition and for calculating the quantile of a vector. The RColorBrewer and grDevices R packages are used to generate color scale for the heatmaps. The cowplot R packages is used to plot mulitple heatmaps into a grid. The compVenn makes use of the VennDiagram R package. compMA and compVolcano use ggplot2 and use gridExtra to plot plot multiple plots into one grid. The package uses data from an RNAseq experiment characterize the human airway smooth muscle transcriptome conducted by University of Pennsylvania as examples and exports them as data available to users.

References

Auguie, B. (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.3. https://CRAN.R-project.org/package=gridExtra

Chen, H. (2021). VennDiagram: Generate High-Resolution Venn and Euler Plots. R package version 1.7.0. https://CRAN.R-project.org/package=VennDiagram

Himes et al. (2014). RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. PloS one, 9(6), e99625. https://doi.org/10.1371/journal.pone.0099625

Kolde, R. (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8

Neuwirth, E. (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. https://doi.org/10.1093/bioinformatics/btp616

Wickham H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

Wilke, C. O. (2020). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.1.1. https://CRAN.R-project.org/package=cowplot

Zhu, A., Ibrahim, J. G., Love, M. I. (2018). Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences Bioinformatics

Acknowledgements

This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinfor- matics, University of Toronto, Toronto, CANADA.