# Deg Screening Analysis

Use when screening differentially expressed genes from a bulk expression matrix between...

- Repository: https://github.com/aipoch/medical-research-skills (2020 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aipoch/medical-research-skills --skill deg-screening-analysis -g -a claude-code -y`
Codex: `npx skills add aipoch/medical-research-skills --skill deg-screening-analysis -g -a codex -y`
Cursor: `npx skills add aipoch/medical-research-skills --skill deg-screening-analysis -g -a cursor -y`
Gemini CLI: `npx skills add aipoch/medical-research-skills --skill deg-screening-analysis -g -a gemini-cli -y`

## Description

Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

# Differential Expression Gene Screening Analysis (Volcano Plot & Clustered Heatmap)

## When to Use

Use this skill when you need a reproducible two-group DEG workflow on a bulk expression matrix and want:
- a full differential expression table
- a filtered DEG table
- a volcano plot
- a clustered heatmap of top differential genes

Typical requests include:
- compare case vs control samples with limma
- screen upregulated and downregulated genes from a normalized expression matrix
- generate a DEG table with volcano and heatmap outputs from bulk transcriptome data

## Out of Scope

Do not use this skill for:
- single-cell RNA-seq workflows
- multi-group contrasts or factorial designs
- count-model pipelines that require `DESeq2` or `edgeR`
- batch correction, covariate-adjusted models, or generalized design-matrix consulting
- non-expression omics data

If the request falls outside this scope, stop and hand off to a more appropriate analysis workflow instead of forcing the data through this skill.

## Practical Caveats

- `Diffanalysis.csv` currently exports `name`, `logFC`, `P.value`, and `P.adj`.
- `--p_type` controls both DEG screening semantics and volcano plot significance semantics.
- `plot/heatmap.pdf` is generated only when at least two heatmap genes remain after ranking.
- When the result is very sparse, prefer keeping tables and volcano output as the primary artifacts.

## When to Read External Files

The rest: https://github.com/aipoch/medical-research-skills/blob/HEAD/awesome-med-research-skills/Data Analysis/deg-screening-analysis/SKILL.md