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name: bulk-rna-seq-deseq2-analysis-with-omicverse title: Bulk RNA-seq DESeq2 analysis with omicverse description: Walk Claude through PyDESeq2-based differential expression, including ID mapping, DE testing, fold-change thresholding, and enrichment visualisation.

Bulk RNA-seq DESeq2 analysis with omicverse

Overview

Use this skill when a user wants to reproduce the DESeq2 workflow showcased in t_deseq2.ipynb. It covers loading raw featureCounts matrices, mapping Ensembl IDs to symbols, running PyDESeq2 via ov.bulk.pyDEG, and exploring downstream enrichment plots.

Instructions

1. Import and format the expression matrix
   • Call import omicverse as ov and ov.utils.ov_plot_set() to standardise visuals.
   • Read tab-separated count data from featureCounts using ov.utils.read(..., index_col=0, header=1).
   • Strip trailing .bam from column names with [c.split('/')[-1].replace('.bam', '') for c in data.columns].
2. Map gene identifiers
   • Ensure the appropriate mapping pair exists by running ov.utils.download_geneid_annotation_pair().
   • Replace gene_id with gene symbols using ov.bulk.Matrix_ID_mapping(data, 'genesets/pair_<GENOME>.tsv').
3. Initialise the DEG object
   • Create dds = ov.bulk.pyDEG(data) from the mapped counts.
   • Resolve duplicate gene names with dds.drop_duplicates_index() and confirm success in logs.
4. Define contrasts and run DESeq2
   • Collect sample labels into treatment_groups and control_groups lists that match column names exactly.
   • Execute dds.deg_analysis(treatment_groups, control_groups, method='DEseq2') to invoke PyDESeq2.
5. Filter and tune thresholds
   • Inspect result shape (dds.result.shape) and optionally filter low-expression genes, e.g. dds.result.loc[dds.result['log2(BaseMean)'] > 1].
   • Set thresholds via dds.foldchange_set(fc_threshold=-1, pval_threshold=0.05, logp_max=6) to auto-pick fold-change cutoffs.
6. Visualise differential genes
   • Draw volcano plots with dds.plot_volcano(...) and summarise key genes.
   • Produce per-gene boxplots: dds.plot_boxplot(genes=[...], treatment_groups=..., control_groups=..., figsize=(2, 3)).
7. Run enrichment analyses (optional)
   • Download enrichment libraries using ov.utils.download_pathway_database() and load them through ov.utils.geneset_prepare.
   • Rank genes for GSEA with rnk = dds.ranking2gsea().
   • Instantiate gsea_obj = ov.bulk.pyGSEA(rnk, pathway_dict) and call gsea_obj.enrichment() to compute terms.
   • Plot enrichment bubble charts via gsea_obj.plot_enrichment(...) and GSEA curves with gsea_obj.plot_gsea(term_num=..., ...).
8. Troubleshooting
   • If PyDESeq2 raises errors about size factors, remind users to provide raw counts (not log-transformed data).
   • gene_id mapping depends on species; direct them to download the correct genome pair when results look sparse.
   • Large pathway libraries may require raising recursion limits or filtering to the top N terms before plotting.

Examples

• "Run PyDESeq2 on treated vs control replicates and highlight the top enriched WikiPathways terms."
• "Filter DEGs to genes with log2(BaseMean) > 1, auto-select fold-change cutoffs, and create volcano and boxplots."
• "Generate the ranked gene list for GSEA and plot the enrichment curve for the top pathway."

References

• Tutorial notebook: t_deseq2.ipynb
• Sample featureCounts matrix: sample/counts.txt
• Quick copy/paste commands: reference.md