Correlation Plot for Gene Expression Analysis

This repository provides a complete guide to generating Correlation Plots for biological data analysis.

Overview

This README provides a complete guide to generating a correlation matrix heatmap for gene expression or similar numerical datasets using R. The plot visualizes pairwise correlations between variables, making it easy to identify positive/negative relationships, clusters, and expression patterns.

Dataset Requirements

To generate the correlation matrix heatmap, the input CSV file must contain numerical values only. Typical input examples include:

• Gene expression values (FPKM, TPM, counts)
• Feature measurements
• Any multivariate numerical dataset

Step-by-Step Tutorial

1. Install Required Packages

   First, install and load the corrplot and tidyverse package. This package helps you make correlation heatmaps. Hmisc package is Used if you want to calculate correlation + p-values.

2. Import Your Data

   Choose and upload your CSV file using file.choose(). Then R will read your file and store it in a variable.

3. Prepare Your Data

   Make sure your dataset has only numbers. Remove any text columns like sample IDs. Also remove rows with missing values so the correlation works correctly.

4. Calculate the Correlation Matrix

   Use the cor() function to calculate correlations between all columns in your dataset. This creates a matrix showing how strongly each variable is related to the others.

5. Create the Correlation Plot

   Use the corrplot() function to draw the heatmap. The colors show positive or negative relationships. Numbers inside the boxes show the correlation values. The labels are rotated so they are easy to read. The variables are grouped using clustering so similar variables appear next to each other.

6. Add a Title to the Plot

   Use the title() function to write a clear title above your plot. This makes your figure more understandable.

Use Cases

1. Gene Expression Correlation Analysis

   Correlation plots help identify co-expressed genes, functional modules, and potential pathways.

2. Feature Relationship Analysis

   Useful for inspecting relationships in metabolomics, proteomics, clinical data, and more.

3. Cluster Discovery

   Hierarchical clustering helps identify groups of variables that behave similarly.

4. Clinical Parameter Correlation

   Analyze relationships among lab measurements like glucose, hemoglobin, cholesterol, etc. Great for finding clinical risk factors or co-varying biomarkers.

5. Metabolomics Correlation

   Identify correlated metabolites and metabolic pathway patterns from abundance matrices. Helps reveal pathway-level regulation trends.

6. Proteomics Correlation

   Explore co-regulated proteins using mass-spec intensity or spectral count values. Useful in pathway discovery and interaction prediction.

7. Microbiome Abundance Correlation

   Detect co-occurrence or exclusion patterns among genera, families, or ASVs. Works once your feature table is numeric.

8. Drug Response Correlation

   Study associations between gene expression and drug sensitivity metrics like IC50 or AUC. Helps identify resistance markers or predictive signatures.