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Review
. 2019 Nov 27;20(6):2044-2054.
doi: 10.1093/bib/bby067.

Interpretation of differential gene expression results of RNA-seq data: review and integration

Adam McDermaid  1 , Brandon Monier  2 , Jing Zhao  3 , Bingqiang Liu  4 , Qin Ma  5   6
Affiliations
Review

Interpretation of differential gene expression results of RNA-seq data: review and integration

Adam McDermaid et al. Brief Bioinform. .

Abstract

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 we reviewed DGE results analysis from a functional point of view for various visualizations. We 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.

Keywords: R/Bioconductor package; bioinformatics tools; differential gene expression analysis; differentially expressed genes; visualization and interpretation.

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Figures

Figure 1
Figure 1
Treatment distributions visualization generated by the ViDGER package using a DESeq2 data set.
Figure 2
Figure 2
Scatter plot of normalized read counts generated by the ViDGER package using a DESeq2 data set.
Figure 3
Figure 3
Heatmap of DEG counts by comparison generated by the ViDGER package using a DESeq2 data set with an adjusted P-value cutoff of 0.05 for classification as differentially expressed.
Figure 4
Figure 4
MA plot displaying the log fold-change compared with mean expression generated by the ViDGER package using a DESeq2 data set, with default log fold-change thresholds of −1 and 1.
Figure 5
Figure 5
Volcano plot generated by the ViDGER package using a DESeq2 data set, with default log fold-change thresholds of −1 and 1 and an adjusted P-value threshold of 0.05.
Figure 6
Figure 6
Four-way plot generated by the ViDGER package using a DESeq2 data set, with default log fold-change thresholds of −1 and 1.
Figure 7
Figure 7
(A) Boxplot generation of RNA-seq data using vsBoxplot; (B) scatter plot 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).
Figure 8
Figure 8
Matrix of all pairwise scatter plots showing normalized expression values generated by the ViDGER package using a DESeq2 data set. In addition to the pairwise scatter plots, density plots are provided along the diagonal and pairwise correlation values are provided in the opposite half of the matrix.
Figure 9
Figure 9
Matrix of all pairwise MA plots showing log fold-change compared with mean expression value generated by the ViDGER package using a DESeq2 data set, with default log fold-change thresholds of −1 and 1.
Figure 10
Figure 10
Matrix of all pairwise Volcano plots showing log fold-change versus adjusted P-value generated by the ViDGER package using a DESeq2 data set, with default log fold-change thresholds of −1 and 1 and an adjusted P-value threshold of 0.05.
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