Bulk Rna Seq Deseq2 Analysis With Omicverse

• Create dds = ov.bulk.pyDEG(data) from the mapped counts.
• Resolve duplicate gene names with dds.drop_duplicates_index() and confirm success in logs.

• 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.

• 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.

• 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)).

• 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=..., ...).

# 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)}"

• 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.