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ngs-bulk-rnaseq-differential-expression

Runs bulk RNA-seq differential-expression analysis from count matrices with design formulas, contrasts, and QC plots.

5.3k|765|Updated Mar 4, 2026
Search Tags:#bioinformatics#differential expression#rna-seq#count matrix#edger#limma-voom#deSeq2
One-click install
npx skills add https://github.com/openai/plugins --skill ngs-bulk-rnaseq-differential-expression
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ngs-bulk-rnaseq-differential-expression
Source: https://github.com/openai/plugins/tree/main/plugins/ngs-analysis/skills/ngs-bulk-rnaseq-differential-expression
Command: npx skills add https://github.com/openai/plugins --skill ngs-bulk-rnaseq-differential-expression

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bulk RNA-seq differential-expression analysis requires careful handling of count matrices, sample metadata, design formulas, contrasts, batch effects, and replication checks, and mistakes in any of these steps produce invalid results. This Skill structures the entire workflow so analyses are planned, validated, and executed with auditable outputs.

Core Features & Use Cases

  • Framework Selection: Chooses between DESeq2, edgeR, or limma-voom based on count type, study design, or lab standard, with an auto mode that detects integer-like counts.
  • Design and Contrast Validation: Verifies replication sufficiency, metadata-to-matrix matching, batch and covariate declarations, and paired-design modeling before running statistics.
  • QC and Reporting: Produces PCA, sample-distance, library-size, and mean-variance plots plus per-contrast result tables, not-tested stubs, and a full run envelope with manifests and logs.
  • Use Case: A researcher with a raw count matrix and sample metadata from a treated-vs-control experiment uses this Skill to confirm the design formula, run DESeq2, and receive volcano plots, ranked gene tables, and a Marimo review app.

Quick Start

Ask the assistant to run bulk RNA-seq differential expression on your count matrix and sample metadata files, specifying your conditions, contrasts, and any batch variables.

Frequently Asked Questions about ngs-bulk-rnaseq-differential-expression

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run bulk RNA-seq differential expression from a count matrix?▼

Provide a raw count matrix and sample metadata file, then define your conditions, replicates, and contrasts. The runner script accepts --count-matrix, --sample-metadata, and --contrasts arguments and executes with DESeq2, edgeR, or limma-voom.

DESeq2 vs edgeR vs limma-voom for RNA-seq analysis?▼

DESeq2 is the default for integer count matrices, edgeR is the fallback for integer-like counts, and limma-voom suits non-integer expression matrices or specific lab standards. Auto mode selects the method based on count type and package availability.

Can I use normalized or log-transformed expression instead of raw counts?▼

Yes, declare the matrix type with --input-mode as raw_counts, normalized_expression, or log_expression. Auto mode infers the type and records a warning when normalization is skipped because the matrix is already transformed.

What happens when a contrast has insufficient replication?▼

The analysis does not run underpowered contrasts silently. Instead it writes explicit .not_tested.tsv stub files documenting contrasts blocked by insufficient replication or confounding, along with caveats in the summary.

How are batch effects and paired samples handled in RNA-seq DE?▼

Batch variables and covariates must be declared explicitly in the design formula, and paired or repeated-measures designs model the subject or donor directly. Visual batch removal is treated separately from batch correction in the statistical model.

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