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From clawbio
Performs differential expression analysis on bulk RNA-seq and pseudo-bulk count matrices using PyDESeq2, with QC, PCA visualization, contrast testing, volcano/MA plots, and markdown reports.
npx claudepluginhub clawbio/clawbio --plugin clawbioHow this skill is triggered — by the user, by Claude, or both
Slash command
/clawbio:rnaseq-deThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill performs differential expression on bulk RNA-seq or pseudo-bulk count matrices.
Differential gene expression analysis using Python DESeq2. Identify DE genes from bulk RNA-seq counts with Wald tests, FDR correction, and volcano/MA plots.
RNA-seq differential expression analysis with R DESeq2 — DEG lists, fold changes, dispersion estimation, design formulas, multi-condition contrasts, and Venn-set operations. Use with a count matrix + metadata to find DEGs or run PCA/clustering.
Performs bulk RNA-seq differential expression analysis with PyDESeq2: loads counts, normalizes, fits negative binomial GLMs, Wald tests (BH-FDR), LFC shrinkage, volcano/MA plots. For two-group or multi-factor designs with batch correction.
Share bugs, ideas, or general feedback.
This skill performs differential expression on bulk RNA-seq or pseudo-bulk count matrices.
.csv or .tsv): rows are genes, columns are samples, first column is gene identifier.csv or .tsv): one row per sample, must include sample_id~ condition or ~ batch + conditionfactor,numerator,denominator (e.g. condition,treated,control)rnaseq_de_report/
├── report.md
├── figures/
│ ├── pca.png
│ ├── volcano.png
│ └── ma_plot.png
├── tables/
│ ├── qc_summary.csv
│ ├── normalized_counts.csv
│ └── de_results.csv
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256
python rnaseq_de.py \
--counts counts.csv \
--metadata metadata.csv \
--formula "~ batch + condition" \
--contrast "condition,treated,control" \
--output report_dir