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differential-expression-analysis

Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.

Published by aipoch ↗aipoch/medical-research-skills

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Use differential-expression-analysis to compare the two groups in [bulk RNA-seq or microarray expression matrix] using [sample-ID and group CSV], and produce differentially expressed genes with volcano and heatmap visualizations; each group must have at least two matching samples.

Required inputs

[bulk RNA-seq or microarray expression matrix]
[sample-ID and group CSV]

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SKILL.md

name: differential-expression-analysisdescription: Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.license: MITauthor: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Differential Expression Analysis

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	`references/algorithm.md`	Statistical methods, formulas, assumptions
Need to run analysis	`scripts/main.R`	Execute: `Rscript scripts/main.R --input_file ... --group_file ...`
Encounter errors	`references/troubleshooting.md`	Common errors and solutions
Need CLI examples	`references/cli-guide.md`	Detailed CLI usage examples
Need test data	`tests/data/`	Sample input files for testing

Usage

Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --output_dir ./output/ \
  --diff_method limma \
  --p_threshold 0.05 \
  --logfc_threshold 0.1 \
  --seed 42

Arguments

Short	Long	Type	Default	Description
`-i`	`--input_file`	character	required	Expression matrix file (genes as rows, samples as columns)
`-g`	`--group_file`	character	required	Group information file (sample ID + group columns)
`-o`	`--output_dir`	character	`./output/`	Output directory
`-m`	`--diff_method`	character	`limma`	Method: limma, deseq2, edger, t, wilcox
`-n`	`--norm_method`	character	`TMM`	Normalization for edgeR: TMM, RLE, upperquartile
`-p`	`--p_threshold`	numeric	`0.05`	P-value threshold
`-f`	`--logfc_threshold`	numeric	`0.1`	Log fold change threshold
`-s`	`--seed`	integer	`42`	Random seed for reproducibility

Input Format

Expression Matrix (input_file)

Genes as rows, samples as columns, CSV format with gene ID in first column.

"","GSM1442228","GSM1442229","GSM1442230"
"0610006L08Rik",3.438,3.237,3.265
"0610007P14Rik",6.734,7.017,6.807

Group File (group_file)

CSV with sample ID and group columns.

"ID","group"
"GSM1442228","Control"
"GSM1442229","Control"
"GSM1442230","DIC"

Output Files

File	Description
`Diffanalysis.csv`	Complete DE results with gene_id, logFC, Pvalue, Padj
`volcano_plot.pdf`	Volcano plot with significance thresholds
`heatmap.pdf`	Heatmap of top upregulated/downregulated genes
`session_info.txt`	R session and package version info
`temp/rdegs.csv`	Significant differentially expressed genes
`temp/Diffanalysis_filtered.csv`	Full results with group annotations

Workflow

Step 1: Validate Input

Check file existence
Validate sample matching between expression matrix and group file
Verify at least 2 samples per group

Step 2: Run Differential Expression

Choose method: limma, DESeq2, edgeR, t-test, or Wilcoxon
Calculate logFC and p-values
Apply multiple testing correction (Benjamini-Hochberg)

Step 3: Filter Results

Filter by p-value and logFC thresholds
Classify genes as Up, Down, or Not significant

Step 4: Generate Visualizations

Volcano plot showing significance vs fold change
Heatmap of top differential genes

Methods

limma

Linear models for microarray and RNA-seq with empirical Bayes moderation. Recommended for normalized expression data (FPKM, TPM).

DESeq2

Negative binomial GLM with variance stabilization. Recommended for raw count data.

edgeR

Empirical Bayes methods with TMM normalization. Supports robust dispersion estimation.

t-test / Wilcoxon

Simple pairwise statistical tests. t-test for parametric, Wilcoxon for non-parametric.

Examples

Basic Usage (limma)

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -m limma

With DESeq2 for Count Data

Rscript scripts/main.R \
  -i count_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -m deseq2

Custom Thresholds

Rscript scripts/main.R \
  -i expression_matrix.csv \
  -g group_info.csv \
  -o ./output \
  -p 0.01 \
  -f 0.5

Error Handling

Common Errors

Error	Cause	Solution
`SKILL_FILE_NOT_FOUND`	Input file doesn't exist	Check file path
`SKILL_SAMPLE_MISMATCH`	Sample names don't match	Verify group file matches expression matrix columns
`SKILL_INVALID_DATA`	Less than 2 groups or samples per group	Check group file
`SKILL_FILTER_ERROR`	No significant genes found	Relax thresholds or check data quality
`SKILL_DEPENDENCY_MISSING`	R package not installed	Install required packages

IF error persists, READ: references/troubleshooting.md

Testing

Test with Sample Data

# Check help
Rscript scripts/main.R --help

# Run with sample data
Rscript scripts/main.R \
  -i tests/data/Combined_Datasets_Matrix_mus.csv \
  -g tests/data/Combined_Datasets_mus_Group.csv \
  -o tests/output/

Validation Commands

# Count lines in output
wc -l output/Diffanalysis.csv

# Check volcano plot exists
ls -la output/volcano_plot.pdf

Implementation Checklist

Last updated: 2026-04-01 | Version: 2.0.0