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?
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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:
library(ggplot2) # 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() + theme_light()