https://github.com/openanalytics/davis
https://github.com/openanalytics/davis
Last synced: 9 days ago
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- Host: GitHub
- URL: https://github.com/openanalytics/davis
- Owner: openanalytics
- Created: 2024-05-29T15:47:09.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2026-02-23T20:36:10.000Z (about 2 months ago)
- Last Synced: 2026-02-24T02:58:51.199Z (about 2 months ago)
- Language: R
- Size: 497 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
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
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("daVis")
```
NOTE: to install development version direct from GitHub:
```r
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("openanalytics/daVis")
```
### 2\. Load the package into R session
```r
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](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450).
```r
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).
```r
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.
```r
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.
```r
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).
```r
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.
```r
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.
```r
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.
```r
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.
```r
coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")
daSignificantGenesBarplot(input = res.limma,
coef = coefs,
coefLabel = c("A", "B", "C", "D"),
addPercentage = TRUE)
```
