Source: 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