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differential-expression-analysis
GitHub用于批量RNA-seq或微阵列表达数据分析，识别两组生物样本间差异表达基因，并提供火山图和热图可视化。
Trigger Scenarios
分析bulk RNA-seq数据以寻找差异基因
生成差异表达基因的火山图或热图
Install
npx skills add aipoch/medical-research-skills --skill differential-expression-analysis -g -y
SKILL.md
Frontmatter
{
"name": "differential-expression-analysis",
"author": "AIPOCH",
"license": "MIT",
"description": "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."
}
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
- CLI parsing with
optparse -
set.seed()for reproducibility -
requireNamespace()dependency checks - Session info recording
- Temp file cleanup
- File reading instructions in SKILL.md
- Modular script structure (<100 lines per file)
- Test data provided
- Error handling with SKILL_* codes
- Scripts in
scripts/directory - References in
references/directory
Last updated: 2026-04-01 | Version: 2.0.0
Version History
- f5ef65b Current 2026-07-24 17:04
