daVis: Visualization of differential expression analysis output

Katarzyna Górczak, Laure Cougnaud 2026-01-29

Introduction

The daVis package contains utility functions to visualize the output from differential expression analysis. The input data can be a model, a list of top tables, or a combination of these two. The model can be of class MArrayLM (limma), DGELRT (edgeR), or DESeqResults (DESeq2).

Installation

1. Download the package from Bioconductor

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("daVis")

NOTE: to install development version direct from GitHub:

if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")

devtools::install_github("openanalytics/daVis")

2. Load the package into R session

library(daVis)

Example data

The createExampleData() function will download and create an example input data for the visualizations.

More information about the experiment can be found here.

tmpDir <- tempfile(); dir.create(tmpDir)
exampleData <- createExampleData(path = tmpDir, output = c("limma", "edgeR", "deseq2", "topTable"))
res.limma <- exampleData$limma
res.edger <- exampleData$edgeR
res.deseq <- exampleData$DESeq2
topTableList <- exampleData$topTable

Visualizations

Volcano plot

The daVolcanoPlot() function enables a quick visual identification of the size and significance of the feature expression effects (top left/right). The significance of the effect is represented by the raw p-value on the y axis, so highly significant features are at the top of the plot. The size of the effect is represented by the log of the fold change (negative/positive for down/up-regulation), so features with high effects are at the right/left side of the plot. Below, the color scale is used for adjusted p-values (corrected for multiple testing across genes).

coefs <- c("B.LvsP", "L.LvsP")

daVolcanoPlot(input = res.limma, 
              coef = coefs, 
              coefLabel = c("A", "B"),
              facetNCol = 2,
              colorVar = "adj.P.Val",
              topGenes = 5,
              topGenesVar = "SYMBOL")

Log-ratio plot

The daLogRatioPlot() function represents the differential effect (e.g. treatment versus control) for several conditions (e.g., compounds or concentrations) of the experiment (log FC scale). This enables to visualize a bigger subset of genes. The significance of genes can be represented via colored rows, e.g., red denotes significant genes, while gray indicates non-significant genes.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daLogRatioPlot(input = res.limma,
               featuresVar = c("SYMBOL", "GENENAME"),
               coef = coefs,
               coefLabel = c("A", "B", "C", "D"),
               facetNCol = 4,
               errorBars = TRUE)

Heatmap

The daHeatmapLogFC() function represents the differential effect (e.g. treatment versus control) for several conditions (e.g., compounds or concentrations) of the experiment. It is a graphical representation of the individual values contained in a matrix as colors. This enables to visualize a bigger subset of genes. The gene label can be colored indicating for example the significance of genes, e.g., black color denotes significant genes, while gray represents non-significant genes.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daHeatmapLogFC(input = topTableList,
               featuresIdVar = "ENTREZID",
               featuresVar = c("SYMBOL", "GENENAME"),
               featuresMaxNChar = 35,
               coef = coefs,
               coefLabel = c("A", "B", "C", "D"))

Upset plot

The daUpset() function is used to represent the overlap (intersection) or difference of the sets of significant genes, down or up-regulated separately, between different differential effects. The different shades of blue are indicative for the number of differential effects (sharing these up- or down-regulated genes).

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daUpset(input = topTableList,
        coef = coefs,
        featuresIdVar = "SYMBOL",
        fdr = 0.05,
        dir = "up",
        ylab = paste("Number of (shared) significantly \n", 
                     "up-regulated genes"),
        xlab = paste("Number of significantly \n", 
                     "up-regulated genes"))

Get feature identifiers from each overlapping set with returnAnalysis = TRUE.

Scatter plot

The daScatterPlot() function visualizes the comparison of the logFC for different differential effects.

coefs <- c("B.LvsP", "L.LvsP")

daScatterPlot(input = topTableList,
              coef = coefs,
              coefLabel = c("A", "B"),
              featuresIdVar = "ENTREZID",
              topGenes = 10,
              topGenesVar = "SYMBOL",
              correlation = TRUE)

Waterfall plot

The daWaterfallPlot() function visualizes the logFC for different differential effects colored by adjusted p-value.

daWaterfallPlot(input = res.limma,
                coef = "B.LvsP",
                coefLabel = "A",
                featuresIdVar = "ENTREZID",
                featuresVar = "SYMBOL",
                colorVar = "adj.P.Val",
                color = c("#e52323", "#32a6d3"),
                fillVar = "adj.P.Val",
                fill = c("#e52323", "#32a6d3"),
                typePlot = "static")

MA plot

The daMAplot() function visualizes the logFC versus log2 mean expression.

coefs <- c("B.LvsP", "L.LvsP")

daMAplot(input = res.limma, 
         coef = coefs, 
         coefLabel = c("A", "B"),
         featuresIdVar = "ENTREZID",
         topGenes = 5,
         topGenesVar = "SYMBOL",
         direction = TRUE, 
         color = c("steelblue", "firebrick", "grey"),
         facetNCol = 2)

Significant genes barplot

The daSignificantGenesBarplot() function visualizes the number of significant genes per comparison.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daSignificantGenesBarplot(input = res.limma, 
                          coef = coefs, 
                          coefLabel = c("A", "B", "C", "D"),
                          addPercentage = TRUE)