gsva-analysis-and-visualization

name	gsva-analysis-and-visualization
description	Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression, single-cell analysis, methylation analysis, clinical diagnosis.
license	MIT
skill-author	AIPOCH

GSVA Analysis And Visualization

When to Use

Use this skill when the user wants one of the following:

• Pathway-level GSVA or ssGSEA analysis from a bulk expression matrix plus a sample group file
• Case-vs-control or treatment-vs-control pathway enrichment comparison using GSVA plus limma
• KEGG or MSigDB pathway analysis for bulk RNA-seq or microarray-like expression data
• Heatmap generation from an existing data/GSVA_list.rda result object
• A reproducible CLI-backed GSVA workflow with saved tables, an .rda object, and a PDF heatmap

Typical request patterns:

• "Run GSVA on my bulk RNA-seq matrix and compare case vs control"
• "Use ssGSEA to score pathways and save the pathway differential results"
• "Generate a KEGG pathway heatmap from my saved GSVA result"
• "Do pathway enrichment with GSVA for these grouped bulk samples"

Execution Model

This is a hybrid skill.

1. Use SKILL.md to verify that the request is in scope.
2. Use scripts/main.R for real execution.
3. Use --mode analyze to compute pathway scores and differential results.
4. Use --mode visualize to reuse an existing data/GSVA_list.rda and generate a heatmap. In visualize mode, GSVA_list.rda must exist in output_dir/data/; run analyze or full mode first if it is missing (SKILL_FILE_NOT_FOUND will be raised otherwise).
5. Use --mode full to run analysis and visualization in one pass.
6. Read reference files only when you need algorithm details, troubleshooting, or additional CLI examples.

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	references/algorithm.md	Understand GSVA, limma, and heatmap generation logic
Need to run analysis or plotting	scripts/main.R	Execute the CLI entry point
Encounter errors	references/troubleshooting.md	Find standard error codes and fixes
Need more CLI examples or the baseline execution record	references/cli-guide.md	Copy ready-to-run commands and review the recorded test run
Need sample input files	tests/data/	Use the bundled demo matrix and group file

When Not to Use

• Gene-level differential expression: use differential-expression-analysis instead
• Single-cell RNA-seq clustering or communication analysis: use sc-clustering or cellchat
• Immune infiltration scoring rather than pathway enrichment: use ssgsea-r or ssgsea_immune
• Clinical diagnosis, treatment selection, or patient-specific interpretation: do not use this skill; ask for a validated clinical workflow or human expert review

If the request falls outside these boundaries, stop and tell the user that this skill only covers bulk expression pathway-level GSVA/ssGSEA analysis plus downstream heatmap visualization.

Method Selection Guide

Choose --method based on your data characteristics:

• gsva: kernel-based enrichment scores suitable for continuous expression data with moderate-to-large sample sizes (≥ 10 samples per group recommended).
• ssgsea: rank-based enrichment scores; less sensitive to outliers and more suitable for noisy data or smaller sample sizes.

For detailed methodological comparison, READ: references/algorithm.md

Usage

Rscript scripts/main.R \
  --mode full \
  --input_file tests/data/expr_matrix.csv \
  --group_file tests/data/group.csv \
  --case_group Tumor \
  --control_group Healthy \
  --species "Homo sapiens" \
  --category C2 \
  --subcategory KEGG \
  --output_dir ./output \
  --seed 42

Arguments

Short	Long	Type	Default	Description
-m	--mode	character	analyze	Run mode: analyze, visualize, or full
-i	--input_file	character	required for analyze/full	Expression matrix file (CSV or TSV, genes as rows, samples as columns)
-g	--group_file	character	required for analyze/full	Sample group file (CSV or TSV with sample and group columns)
-a	--case_group	character	required for analyze/full	Case or treatment group label
-c	--control_group	character	required for analyze/full	Control group label
-o	--output_dir	character	./output/	Output directory
-s	--species	character	Homo sapiens	MSigDB species
-C	--category	character	C2	MSigDB category
-S	--subcategory	character	KEGG	MSigDB subcategory
	--method	character	gsva	GSVA method: gsva or ssgsea (see Method Selection Guide above)

Input Format

Expression Matrix

• CSV or TSV file
• First column contains gene identifiers
• Remaining columns are sample names
• Values must be numeric and contain no missing values

Example:

gene,S1,S2,S3,S4
TP53,8.1,7.9,6.5,6.3
EGFR,5.2,5.0,4.2,4.1

The bundled tests/data/expr_matrix.csv is derived from the public GEO series GSE44076 after probe-to-gene collapsing and contains the Tumor versus Healthy subset.

Group File

• CSV or TSV file with a header row
• One sample column: sample, sample_name, or sample_id
• One group column: group, condition, cluster, or class
• Sample names must match the expression matrix columns

Example:

sample,group
GSM1077746,Tumor
GSM1077747,Tumor
GSM1077598,Healthy
GSM1077599,Healthy

Output Files

File	Description
table/GSVA_diff.csv	limma differential pathway results with logFC, P.Value, and adj.P.Val
table/GSVA_enrichment_results.csv	Full GSVA score matrix
table/GSVA_enrichment_results_topN.csv	Top pathway score matrix selected by --top_n and --fdr_threshold
data/GSVA_list.rda	Saved gsva_result object for downstream visualization
plot/GSVA_heatmap.pdf	Heatmap PDF generated in visualize or full mode
session_info.txt	R session and package version information
output_manifest.txt	Append-only manifest of generated outputs across runs in the same output_dir
run_record.txt	Append-only run log with parameters, runtime, and output summaries across runs in the same output_dir

table/GSVA_diff.csv

Column	Type	Description
logFC	numeric	limma-estimated pathway score difference between case and control
AveExpr	numeric	Average pathway score across all samples
t	numeric	Moderated t statistic from limma
P.Value	numeric	Raw p-value from limma
adj.P.Val	numeric	Benjamini-Hochberg adjusted p-value
B	numeric	Log-odds that the pathway is differentially enriched
geneset	character	Pathway identifier used in the GSVA run

Workflow

Step 1: Validate Input

• Check that the expression matrix and group file exist
• Validate supported columns and matching sample names
• Validate CLI ranges and mode-specific required parameters

Step 2: Run Pathway Analysis

• Load MSigDB gene sets for the requested species and collection
• Compute GSVA or ssGSEA scores for each sample
• Fit a limma model for the case-vs-control pathway comparison

Step 3: Generate Output

• Save the full score matrix, top pathway subset, and differential results to table/
• Save the reusable gsva_result object to data/GSVA_list.rda
• Generate the heatmap PDF in plot/ when running visualize or full
• Append a new section to output_manifest.txt and run_record.txt for each invocation so earlier provenance is preserved when reusing one output_dir

Examples

Basic Usage

Rscript scripts/main.R \
  --mode full \
  --input_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --case_group treatment \
  --control_group control \
  --output_dir ./output

With ssGSEA and Custom Parameters

Rscript scripts/main.R \
  --mode analyze \
  --input_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --case_group treatment \
  --control_group control \
  --method ssgsea \
  --top_n 30 \
  --fdr_threshold 0.1 \
  --output_dir ./ssgsea_output \
  --seed 123

Reuse a Saved Result Object

Rscript scripts/main.R \
  --mode visualize \
  --output_dir ./output \
  --plot_file custom_heatmap.pdf \
  --top_up 10 \
  --top_down 10 \
  --top_mode both

For the bundled real-data baseline record, READ: references/cli-guide.md

Error Handling

Error Code	Meaning	Solution
SKILL_FILE_NOT_FOUND	Input file or saved result file is missing; in visualize mode, GSVA_list.rda must exist in output_dir/data/ — run analyze or full mode first	Check the path and rerun with the correct file
SKILL_MISSING_COLUMNS	Group file lacks a valid sample or group column	Rename the columns to a supported name
SKILL_SAMPLE_MISMATCH	Sample names do not match between files	Align sample names before running the skill
SKILL_EMPTY_DATA	Input matrix, gene set query, or plotting matrix is empty	Verify the input matrix and MSigDB settings
SKILL_INVALID_PARAMETER	A CLI argument is missing or out of range	Review the parameter table and rerun
SKILL_PACKAGE_NOT_FOUND	Required R packages are not installed	Install the missing packages listed in references/cli-guide.md

If the error persists, READ: references/troubleshooting.md

Input Validation

This skill accepts:

• A bulk expression matrix file in CSV or TSV format with genes as rows and samples as columns
• A sample group file with one supported sample column and one supported group column
• A valid case/control comparison for pathway-level GSVA or ssGSEA analysis
• Optional heatmap customization parameters for visualization of a saved GSVA_list.rda

Privacy and data-handling note:

• If your matrix or group file can be linked to patients or protected records, anonymize it before use
• This workflow writes result tables, a saved R object, plots, and session metadata to the local output_dir
• Review local output retention practices before using sensitive material

If the user's request does not involve bulk expression pathway enrichment analysis or GSVA heatmap generation — for example, asking for single-cell analysis, gene-level DE testing, methylation analysis, or clinical diagnosis — do not proceed with this workflow. Instead respond:

"gsva-analysis-and-visualization is designed for bulk expression pathway-level GSVA/ssGSEA analysis and saved-result heatmap visualization. Your request appears to be outside this scope. Please provide a bulk expression matrix plus sample group file for GSVA/ssGSEA analysis, or use a more appropriate skill for your task."

Testing

Rscript scripts/main.R --help

Rscript tests/run_tests.R

Rscript scripts/main.R \
  --mode full \
  --input_file tests/data/expr_matrix.csv \
  --group_file tests/data/group.csv \
  --case_group Tumor \
  --control_group Healthy \
  --species "Homo sapiens" \
  --category C2 \
  --subcategory KEGG \
  --output_dir tests/output \
  --seed 42

Expected outputs:

• tests/output/table/GSVA_diff.csv
• tests/output/table/GSVA_enrichment_results.csv
• tests/output/table/GSVA_enrichment_results_topN.csv
• tests/output/data/GSVA_list.rda
• tests/output/plot/GSVA_heatmap.pdf
• tests/output/session_info.txt
• tests/output/output_manifest.txt
• tests/output/run_record.txt

Optional post-check:

Rscript tests/test_skill.R tests/output

tests/run_tests.R executes the full demo workflow, validates the expected output files, then reruns visualize in the same output_dir to confirm that output_manifest.txt and run_record.txt preserve both run sections.

References

1. Hanzelmann S, Castelo R, Guinney J. (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. doi:10.1186/1471-2105-14-7
2. Ritchie ME, Phipson B, Wu D, et al. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. doi:10.1093/nar/gkv007
3. Liberzon A, Birger C, Thorvaldsdottir H, et al. (2015) The Molecular Signatures Database Hallmark Gene Set Collection. Cell Systems. doi:10.1016/j.cels.2015.12.004

For detailed algorithm notes, READ: references/algorithm.md

Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• Only CRAN/Bioconductor packages
• Documented parameters match script
• get_script_dir() defined before any call to it
• File reading instructions in SKILL.md
• Test data provided in tests/data/
• Error handling implemented with SKILL_* messages
• Rscript scripts/main.R --help works