Visualize differential gene expression with ViDGER

Differential gene expression (DGE) analysis is one of the most common applications of RNA-seq data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time-consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. To address this challenge, Adam McDermaid and his colleagues from South Dakota State University have developed ViDGER. Here, they present their tool and its main features.

 

Interpretation and Visualization of Differential Gene Expression through ViDGER

 

One of the most straightforward ways to gain a broader understanding of the tens-of-thousands of pieces of information generated through DGE analysis is to present the results in a graphical format. ViDGER (Visualization of Differential Gene Expression Results using R) provides easy-to-use functions that automatically format informative, high-quality figures to present DGE results from three of the most widely used and highest performing DGE tools: Cuffdiff, DESeq2, and edgeR.

ViDGER Features

 

The ViDGER package includes nine functions, each of which is capable of using Cuffdiff, DESeq2, or edgeR objects. The figures are broken down into two tiers representing the complexity and required information. Tier I include normalized count boxplot and whisker plots (Figure 1A), scatterplots (Figure 1B), DEG matrices (Figure 1C), and scatterplot matrices.  These figures require relatively basic information based on normalized read counts and DEG counts. The scatterplot matrix function provides all possible pairwise-condition scatterplots, along with correlation scores and density plots. Tier II figures include MA-plots (Figure 1D), Volcano plots (Figure 1E), Four-Way plots (Figure 1F), and matrix functionalities for the MA- and Volcano plots. These figures provide broad views of the information related to log fold-changes, adjusted p-values, and mean expression values. To aid in the interpretability of these figures, the ViDGER package functions automatically format data points to indicate log fold-change and adjusted p-value thresholds.

 

The functions included in the ViDGER package were designed to require as little information as possible to facilitate ease-of-use and efficient generation of interpretable figures. Most of the functions only require an indication of the DGE results file and tool type (i.e. Cuffdiff, DESeq2, or edgeR), while others require slightly more information such as which conditions to compare.

 

The MA-, Volcano, and Four-way plots also provide parameterizations that allow for data extraction based on the information used to generate each figure, including log fold-changes, adjusted p-values, condition-specific mean expression values, and indications of whether the ID is differentially expressed or not.

 

 

 Boxplot generation of RNA-seq data using vsBoxplot
Figure 1. (A) Boxplot generation of RNA-seq data using vsBoxplot; (B) scatterplot generation using vsScatterPlot; (C) differential gene expression matrix using vsDEGMatrix; (D) MA plot generation using vsMAPlot; (E) volcano plot generation using vsVolcano; (F) four-way plot generation using vsFourWay. Arrow and text color refer to visualizations generated using Cuffdiff data (black), DESeq2 data (blue), and edgeR data (red).

Key points

 

  • The ViDGER R package provides a straightforward method for visualizing DGE results files.
  • This package integrates DGE results from the three most commonly used DGE tools: DESeq2, edgeR, & Cuffdiff.
  • Nine functions are provided, including six distinct visualizations with three matrix options.
  • The generated visualizations provide comprehensive views of the DGE results files in highly- informative, publication-quality figures, all of which can be extracted in multiple formats.
  • ViDGER also provides a useful method for extracting relevant data from the generated figures, which is useful for further interpretation of the DGE results.

 

Reference

 

McDermaid et al. (2018). ViDGER: An An R package for integrative interpretation of differential gene expression results of RNA-seq data. bioRxiv.