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Get Started Free →Guide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment. Use when a user has bulk count matrices and needs differential expression analysis in omicverse.
.claude/skills/bulk-rna-seq-differential-expression-with-omicverse/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | — | — |
| case-11 | ✗→✓ | ▲ Improved | — | — |
| case-17 | ✗→✓ | ▲ Improved | — | — |
| case-19 | ✗→✓ | ▲ Improved | — | — |
| case-08 | ✗→✓ | ▲ Improved | — | — |
Follow this skill to run the end-to-end differential expression (DEG) workflow showcased in t_deg.ipynb. It assumes the user provides a raw gene-level count matrix (e.g., from featureCounts) and wants to analyse bulk RNA-seq cohorts inside omicverse.
omicverse as ov, scanpy as sc, and matplotlib.pyplot as plt.ov.plot_set() so downstream plots adopt omicverse styling.ov.utils.download_geneid_annotation_pair() and store them under genesets/.ov.pd.read_csv(..., sep='\t', header=1, index_col=0)..bam segments from column names using list comprehension so sample IDs are clean.ov.bulk.Matrix_ID_mapping(counts_df, 'genesets/pair_<GENOME>.tsv') to replace gene_id entries with gene symbols.dds = ov.bulk.pyDEG(mapped_counts).dds.drop_duplicates_index() to keep the highest expressed version.dds.normalize() to calculate DESeq2 size factors, correcting for library size and batch differences.dds.deg_analysis(treatment_groups, control_groups, method='ttest') for the default Welch t-test.method='edgepy' for edgeR-like tests and method='limma' for limma-style modelling.dds.result.loc[dds.result['log2(BaseMean)'] > 1] when needed.dds.foldchange_set(fc_threshold=-1, pval_threshold=0.05, logp_max=6) (fc_threshold=-1 auto-selects based on log2FC distribution).dds.plot_volcano(title=..., figsize=..., plot_genes=... or plot_genes_num=...) to highlight key genes.dds.plot_boxplot(genes=[...], treatment_groups=..., control_groups=..., figsize=..., legend_bbox=...); adjust y-axis tick labels if required.ov.utils.download_pathway_database().ov.utils.geneset_prepare(<path>, organism='Mouse'|'Human'|...).dds.result.loc[dds.result['sig'] != 'normal'].index.ov.bulk.geneset_enrichment(gene_list=deg_genes, pathways_dict=..., pvalue_type='auto', organism=...). Encourage users without internet access to provide a background gene list.ov.bulk.geneset_plot(...) and combine multiple ontologies using ov.bulk.geneset_plot_multi(enr_dict, colors_dict, num=...).dds.result and enrichment tables to CSV for downstream reporting.plt.savefig(...)) when running outside notebooks.treatment_groups/control_groups exactly match column names post-cleanup.omicverse, pyComplexHeatmap, gseapy) are installed for enrichment visualisations.t_deg.ipynbsample/counts.txtreference.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-23 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +78 percentage points is the difference between those two pass rates over the 23 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.