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**Installation**:
```bash
pip install pydeseq2 gseapy pandas numpy scipy anndata

Metadata columns: [Strain, Media, Replicate]
Question asks: "strain effects"

❌ WRONG: design="~Strain" (ignores Media!)
✅ CORRECT: design="~Media + Strain" (accounts for media variation)

Why: Even though question doesn't mention media, it systematically
affects expression. DESeq2 must remove media effects to properly test strain.

import pandas as pd
from scripts.load_count_matrix import load_count_matrix, validate_inputs

**Key considerations**:
- Ensure samples as rows, genes as columns (PyDESeq2 requirement)
- Verify integer counts (round if needed)
- Align sample names between counts and metadata
- Remove zero-count genes

See [references/data_loading.md](references/data_loading.md) for detailed data handling.

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This step prevents missing hidden batch effects that could invalidate your analysis.

from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

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**Why this matters**: DESeq2 will model media effects separately, removing media-driven variance before testing strain differences. This increases statistical power and prevents false positives/negatives.

**When to use what**:
- **Python (PyDESeq2)**: Use for ALL DESeq2 analysis (normalization, testing, filtering)
- **ToolUniverse**: Use ONLY for gene annotation (ID conversion, pathway context)
- **gseapy**: Use for enrichment analysis (GO/KEGG/Reactome)

See [references/pydeseq2_workflow.md](references/pydeseq2_workflow.md) for complete PyDESeq2 patterns including batch effects, interaction terms, and complex designs.

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**Common filtering patterns**:
- Basic DEG count: `len(sig_genes)`
- Specific gene value: `results.loc['GENE_NAME', 'log2FoldChange']`
- Set operations: Use Python sets for "unique", "shared", "overlap"

See [references/result_filtering.md](references/result_filtering.md) for advanced filtering.

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**Dispersion column mapping**:
- "prior to fitting" → `genewise_dispersions`
- "fitted dispersions" → `fitted_dispersions`
- "after shrinkage" / "MAP" → `MAP_dispersions`
- "final" → `dispersions`

See [references/dispersion_analysis.md](references/dispersion_analysis.md) for diagnostics.

import gseapy as gp

**Library selection**:
- Human GO: `GO_Biological_Process_2023`
- Mouse KEGG: `KEGG_2019_Mouse`
- Human KEGG: `KEGG_2021_Human`
- Reactome: `Reactome_2022`

See [references/enrichment_analysis.md](references/enrichment_analysis.md) for complete enrichment workflows.

from tooluniverse import ToolUniverse

tu = ToolUniverse()
tu.load_tools()

**Do NOT use ToolUniverse for**:
- Differential expression (use PyDESeq2)
- Statistical testing (use scipy.stats)
- Enrichment analysis (use gseapy)

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See [references/output_formatting.md](references/output_formatting.md) for all format patterns.

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degs = results[(results['padj'] < 0.05) & (results['log2FoldChange'].abs() > 0.5)]
answer = len(degs)

answer = round(results.loc['GENE_X', 'log2FoldChange'], 2)

up_degs = results[(results['padj'] < 0.05) & (results['log2FoldChange'] > 0)]
answer = len(up_degs)

degs_A = set(results_A[results_A['padj'] < 0.05].index)
degs_B = set(results_B[results_B['padj'] < 0.05].index)
unique_A = degs_A - degs_B
answer = len(unique_A)

genewise = dds.var['genewise_dispersions']
answer = (genewise < 1e-5).sum()

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**Default behavior**: Uses gseapy (no R dependencies required). This is sufficient for most analyses and provides valid enrichment results.

**When to use R clusterProfiler**:
- When exact reproducibility with R-based benchmarks is required
- When collaborating with R users who need identical results
- When specific clusterProfiler features (GSEA, compareCluster) are needed

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