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bulk-rna-seq-deseq2-analysis-with-omicverse

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PyDESeq2 differential expression: ID mapping, DE testing, fold-change thresholding, and GSEA enrichment visualization in OmicVerse.

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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].
  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. Defensive validation 
    # 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)}"
  9. 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