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ngs-bulk-rnaseq-differential-expression

Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.

openai/plugins

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SKILL.md3.5 KB
agents/openai.yaml268 B

SKILL.md（原文）

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Bulk RNA-seq Differential Expression

Use this skill when the user has raw counts or a count-generation output and wants differential expression, contrasts, QC plots, or ranked gene tables.

Essential Inputs

Confirm:

raw count matrix path and sample metadata path
gene ID type and annotation mapping requirement
biological conditions, replicates, batch variables, donor pairing, covariates, and exclusions
exact contrasts and baseline levels
preferred statistical framework: DESeq2, edgeR, limma-voom, or existing lab standard
output needs: normalized counts, PCA, sample distance, volcano plots, heatmaps, ranked tables, GSEA-ready lists

Preconditions

Do not start differential expression until:

raw counts are preserved
each requested contrast has enough biological replication
sample metadata row names match count matrix columns
batch/covariate choices are explicit
exploratory PCA/sample-distance plots do not reveal obvious swaps or failed libraries

Route

For most count matrices, use DESeq2 or edgeR. Use limma-voom when the study design or lab standard favors it. Keep the analysis in R when using Bioconductor unless the user specifically asks for a Python-only workflow.

The plugin-owned local runner is:

python plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \
  --count-matrix count_matrix.tsv \
  --sample-metadata sample_metadata.tsv \
  --contrasts contrasts.tsv \
  --execute

Use --method auto unless the user or lab standard specifies DESeq2, edgeR, or limma_log2. Auto mode uses DESeq2 when integer-like counts and the package are available, falls back to edgeR for integer-like counts, and uses limma_log2 for non-integer expression matrices.

Use --input-mode to declare whether the matrix is raw_counts, normalized_expression, or log_expression. When --input-mode auto is used, the runner infers the mode and records a warning if normalization is skipped because the matrix is already transformed.

Preflight command:

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan

Decision Points

Never compare groups without stating the design formula and contrast.
Treat batch correction in modeling separately from visual batch removal.
Do not filter genes using post-hoc knowledge of the contrast.
For paired or repeated-measures designs, model subject/donor explicitly.
Report genes with effect size, uncertainty, adjusted p-value, and filtering status.

Outputs

Produce:

design formula and contrast manifest
QC plots: library size, detected genes, PCA/sample distance, mean-variance trend, and outlier review
input-mode-aware matrix exports plus the modeling/log-scale matrix used for DE
differential-expression tables per contrast
explicit .not_tested.tsv stubs for contrasts blocked by insufficient replication or confounding
auto-launched localhost Marimo review app recorded in notebooks/marimo_server.json
caveats for small n, confounded designs, failed samples, or batch variables that cannot be estimated
standard run envelope: run_manifest.json, config.json, validation/, logs/, versions/, visualizations/, notebooks/, artifact_index.json, and summary.md

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