bio-de-visualization

Scope

This skill covers DE-specific built-in functions:

• DESeq2: plotMA(), plotPCA(), plotDispEsts(), plotCounts()
• edgeR: plotMD(), plotBCV(), plotMDS()
• Sample distance heatmaps and p-value distributions

For custom ggplot2/matplotlib implementations of volcano, MA, and PCA plots, see data-visualization/specialized-omics-plots.

Installation

install.packages(c('ggplot2', 'pheatmap', 'RColorBrewer', 'ggrepel'))

## MA Plot

**Goal:** Visualize the relationship between mean expression and log fold change to assess DE results.

**Approach:** Plot log fold change against mean normalized counts, highlighting significant genes.

**"Make an MA plot of my DE results"** → Plot mean expression vs. fold change with significant genes colored, using plotMA or ggplot2.