omicverse/omicverse-skills/bulk-deseq2-analysis

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.style() to standardise visuals.
• Read tab-separated count data from featureCounts using ov.io.read(..., index_col=0, header=1).
• Strip trailing .bam from column names with [c.split('/')[-1].replace('.bam', '') for c in data.columns].

1. 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').

1. 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.

1. 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.

1. 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.

1. 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)).

1. 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=..., ...).

1. Defensive validation

``python # Before PyDESeq2: verify count matrix contains raw integers (not log-transformed) import numpy as np if hasattr(data, 'values'): sample = data.values.flatten()[:1000] else: sample = np.array(data).flatten()[:1000] if np.any(sample != sample.astype(int)): print("WARNING: Data may not be raw counts. PyDESeq2 requires integer counts, not log-transformed.") # Verify treatment/control groups match column names for g in treatment_groups + control_groups: assert g in data.columns, f"Sample '{g}' not in count matrix columns: {list(data.columns)}" ``

1. 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: see the tutorial page for example count inputs: t_deseq2
• Quick copy/paste commands: `reference.md`