Gsva Analysis And Visualization

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

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

bashCopy code
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

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:

csvCopy code
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:

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

Output Files

table/GSVA_diff.csv

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

bashCopy code
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

bashCopy code
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

bashCopy code
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

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

bashCopy code
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:

bashCopy code
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