Ridge Plot

This repository provides a complete guide to generating ridge plots for visualizing gene expression distributions across multiple biological conditions.

Overview

A ridge plot is a layered visualization that displays multiple density curves stacked vertically, each representing the distribution of a numeric variable for a different group. It combines smooth density estimation with overlapping ridges to create a clear, compact view of how values are distributed across categories. The plot is characterized by its flowing, wave-like appearance, making it visually intuitive and easy to interpret.

Dataset Requirements

To create a ridge plot for gene expression analysis, your CSV file must include:

• condition – The biological group (e.g., Normal, Tumor, Treated, Control)
• expression – Expression level of the selected gene (e.g., TPM, FPKM, counts-normalized)

Step-by-Step Tutorial

1. Install Required Packages

   Install ggplot2 and ggridges (or ensure they are already installed) so your R environment can create ridge plots and handle plotting themes. Restart R if necessary after installation.

2. Import Your Gene Expression Data

   Open and load your CSV file so that the condition and expression columns are available in your workspace. Make sure column names are consistent and missing values are handled.

3. Prepare the Data

   Check that each condition has enough samples and that expression values are on a comparable scale (e.g., normalized counts or TPM). Optionally filter extreme outliers or low-quality samples.

4. Create the Ridge Plot

   Choose the gene to visualize and generate a ridge plot where each condition shows a smoothed distribution of expression values. Adjust plot labels, ordering of conditions, and legend for clarity.

5. Customize & Export

   Tune aesthetics (titles, axis labels, color palette, and ordering) to match your presentation needs, then save the figure.

Use Cases

1. Differential Gene Expression Comparison

   Ridge plots help compare how a gene is expressed across multiple biological groups by showing full distribution patterns instead of just averages. This makes differences between Normal, Tumor, Treated, or other conditions easy to spot visually.

2. Detecting Variability Within Groups

   They reveal how much variation exists inside each condition, showing spread, peaks, and unusual patterns. This helps identify heterogeneity in patient groups or treatment responses.

3. Visualizing Many Conditions Together

   Ridge plots allow you to stack many conditions in one clean figure, making it easier to compare multiple tissues, disease stages, or treatments at once without creating separate plots.

4. Identifying Biomarker Patterns

   They highlight conditions where a gene is consistently high or low, helping detect potential biomarkers or candidate genes that show distinct expression trends across groups.