Interpretation of differential gene expression results of RNA-seq data

Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive 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.

Here researchers from South Dakota State University reviewed DGE results analysis from a functional point of view for various visualizations. They also provide an R/Bioconductor package, Visualization of Differential Gene Expression Results using R, which generates information-rich visualizations for the interpretation of DGE results from three widely used tools, Cuffdiff, DESeq2 and edgeR. The implemented functions are also tested on five real-world data sets, consisting of one human, one Malus domestica and three Vitis riparia data sets.

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).

Availability – ViDGER is a package developed for the R environment (>= 3.3.2) and is freely available at: https://github.com/btmonier/vidger.