How to visualise differential expression analysis


Hiya, new here,

Im trying to decide how to visualise Deseq2 results. I want to plot the relationship between Log2 Fold change in expression against Base mean and/or gene length. I have the data for these parameters. Is there a cool graph type on ggplot that is purpose built for this kind of thing?

many thanks!





Loosely related, I usually try to show the degree of overlap of the datapoints. A solution is a heatmap-like colour scheme with red meaning higher density of points. If any useful this is an example of code to do that:


# Some simulated data
dge <- data.frame(
    logFC= rnorm(20000),
    logCPM= rnorm(20000)

# Assign colour to points according to density
dge$coldens <- densCols(dge$logCPM, dge$logFC, colramp = colorRampPalette(rev(rainbow(10, end = 4/6))))

# Then plot as usual with base R. With ggplot set scale_color_identity
gg<- ggplot(data= dge, aes(x= logCPM, y= logFC)) +
    geom_point(aes(logCPM, logFC, col = coldens), size = 0.5, shape= 20, stroke= 0, alpha= 1) +
    scale_color_identity() +

enter image description here

Most people do volcano plots of fold change along the X, and the log of the padj along the Y.

before adding your answer.

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