usage-guide.md

DE Visualization - Usage Guide

Overview

This skill covers creating publication-quality visualizations for differential expression results, including MA plots, volcano plots, PCA plots, and heatmaps. Works with both DESeq2 and edgeR output.

Prerequisites

install.packages(c('ggplot2', 'pheatmap', 'RColorBrewer', 'ggrepel'))
BiocManager::install('EnhancedVolcano')  # Optional

Quick Start

Tell your AI agent what you want to do:

• "Create a volcano plot from my DESeq2 results"
• "Make a heatmap of the top 50 differentially expressed genes"
• "Generate a PCA plot colored by treatment group"

Example Prompts

MA and Volcano Plots

"Create an MA plot highlighting significant genes"

"Make a volcano plot with gene labels for top hits"

"Generate an EnhancedVolcano plot for my results"

PCA and Sample Clustering

"Show PCA of my samples colored by condition and shaped by batch"

"Create a sample distance heatmap"

"Plot MDS for my edgeR data"

Heatmaps

"Make a heatmap of significant genes clustered by expression"

"Create a heatmap for my genes of interest"

"Show expression patterns across samples"

Individual Genes

"Plot counts for gene X across conditions"

"Show expression of my candidate genes"

What the Agent Will Do

1. Extract and format results from DESeq2/edgeR
2. Apply appropriate transformations (vst, log)
3. Create publication-quality figures
4. Add annotations and labels
5. Save in requested format (PDF, PNG)

Common Plot Types

Plot	Shows	Use For
MA plot	LFC vs expression	QC, global view
Volcano	LFC vs significance	Identifying top genes
PCA	Sample relationships	Batch effects, outliers
Heatmap	Expression patterns	Gene clusters, validation

Tips

• Always use variance-stabilized counts (vst) for PCA and heatmaps
• Scale heatmap rows (z-score) for comparable gene patterns
• Check p-value histogram for analysis quality
• Use colorblind-friendly palettes for publications
• Save vector formats (PDF) for publications, raster (PNG) for presentations