pydeseq2

name	pydeseq2
description	Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
allowed-tools	Read Write Edit Bash
compatibility	Requires Python >=3.11 and PyDESeq2 0.5.4-compatible dependencies. Examples target PyDESeq2 0.5.x, formulaic design strings, explicit contrasts, and uv-based installs.
license	MIT license
metadata	{"version":"1.3","skill-author":"K-Dense Inc."}

PyDESeq2

Overview

PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including formulaic single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.

When to Use This Skill

This skill should be used when:

• Analyzing bulk RNA-seq count data for differential expression
• Comparing gene expression between experimental conditions (e.g., treated vs control)
• Performing multi-factor designs accounting for batch effects or covariates
• Converting R-based DESeq2 workflows to Python
• Integrating differential expression analysis into Python-based pipelines
• Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"

Quick Start Workflow

For users who want to perform a standard differential expression analysis:

import pandas as pd
from pydeseq2.dds import DeseqDataSet
from pydeseq2.default_inference import DefaultInference
from pydeseq2.ds import DeseqStats

# 1. Load data
counts_df = pd.read_csv("counts.csv", index_col=0).T  # Transpose to samples × genes
metadata = pd.read_csv("metadata.csv", index_col=0)

# 2. Filter low-count genes
genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]

# 3. Make the reference level explicit and fit DESeq2
metadata["condition"] = pd.Categorical(
    metadata["condition"], categories=["control", "treated"]
)
inference = DefaultInference(n_cpus=4)
dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True,
    inference=inference,
)
dds.deseq2()

# 4. Perform statistical testing
ds = DeseqStats(
    dds,
    contrast=["condition", "treated", "control"],
    inference=inference,
)
ds.summary()

# 5. Access results
results = ds.results_df
significant = results[results.padj < 0.05]
print(f"Found {len(significant)} significant genes")

Core Workflow Steps

The six steps, with code, are in references/core_workflow_steps.md:

1. Data preparation — raw integer counts with genes as columns and samples as rows, and matching metadata. Never feed normalized or transformed values to DESeq2.
2. Design specification — the design factors and the reference level for each.
3. DESeq2 fitting — size factors, dispersions, and the GLM fit.
4. Statistical testing — Wald tests for a named contrast.
5. Optional LFC shrinkage — for ranking and visualization.
6. Result export — the results table with adjusted p-values.

Multi-factor designs, contrasts, and interaction terms are in references/analysis_patterns.md.

Using the Analysis Script

This skill includes a complete command-line script for standard analyses:

# Basic usage
python scripts/run_deseq2_analysis.py \
  --counts counts.csv \
  --metadata metadata.csv \
  --design "~condition" \
  --contrast condition treated control \
  --output results/

# With additional options
python scripts/run_deseq2_analysis.py \
  --counts counts.csv \
  --metadata metadata.csv \
  --design "~batch + condition" \
  --contrast condition treated control \
  --output results/ \
  --min-counts 10 \
  --alpha 0.05 \
  --n-cpus 4 \
  --shrink-coeff "condition[T.treated]" \
  --plots

Script features:

• Automatic data loading and validation
• Gene and sample filtering
• Complete DESeq2 pipeline execution
• Statistical testing with customizable parameters
• Result export (CSV and portable AnnData/H5AD)
• Explicit LFC shrinkage coefficient support for PyDESeq2 0.5.x
• Optional visualization (volcano and MA plots)

Refer users to scripts/run_deseq2_analysis.py when they need a standalone analysis tool or want to batch process multiple datasets.

Result Interpretation

Identifying Significant Genes

# Filter by adjusted p-value
significant = ds.results_df[ds.results_df.padj < 0.05]

# Filter by both significance and effect size
sig_and_large = ds.results_df[
    (ds.results_df.padj < 0.05) &
    (abs(ds.results_df.log2FoldChange) > 1)
]

# Separate up- and down-regulated
upregulated = significant[significant.log2FoldChange > 0]
downregulated = significant[significant.log2FoldChange < 0]

print(f"Upregulated: {len(upregulated)}")
print(f"Downregulated: {len(downregulated)}")

Ranking and Sorting

# Sort by adjusted p-value
top_by_padj = ds.results_df.sort_values("padj").head(20)

# Sort by absolute fold change (use shrunk values)
ds.lfc_shrink(coeff="condition[T.treated]")
ds.results_df["abs_lfc"] = abs(ds.results_df.log2FoldChange)
top_by_lfc = ds.results_df.sort_values("abs_lfc", ascending=False).head(20)

# Sort by a combined metric
ds.results_df["score"] = -np.log10(ds.results_df.padj) * abs(ds.results_df.log2FoldChange)
top_combined = ds.results_df.sort_values("score", ascending=False).head(20)

Quality Metrics

# Check normalization (size factors should be close to 1)
print("Size factors:", dds.obs["size_factors"])

# Examine dispersion estimates
import matplotlib.pyplot as plt
plt.hist(dds.var["dispersions"], bins=50)
plt.xlabel("Dispersion")
plt.ylabel("Frequency")
plt.title("Dispersion Distribution")
plt.show()

# Check p-value distribution (should be mostly flat with peak near 0)
plt.hist(ds.results_df.pvalue.dropna(), bins=50)
plt.xlabel("P-value")
plt.ylabel("Frequency")
plt.title("P-value Distribution")
plt.show()

Visualization Guidelines

Volcano Plot

Visualize significance vs effect size:

import matplotlib.pyplot as plt
import numpy as np

results = ds.results_df.copy()
results["-log10(padj)"] = -np.log10(results.padj)

plt.figure(figsize=(10, 6))
significant = results.padj < 0.05

plt.scatter(
    results.loc[~significant, "log2FoldChange"],
    results.loc[~significant, "-log10(padj)"],
    alpha=0.3, s=10, c='gray', label='Not significant'
)
plt.scatter(
    results.loc[significant, "log2FoldChange"],
    results.loc[significant, "-log10(padj)"],
    alpha=0.6, s=10, c='red', label='padj < 0.05'
)

plt.axhline(-np.log10(0.05), color='blue', linestyle='--', alpha=0.5)
plt.xlabel("Log2 Fold Change")
plt.ylabel("-Log10(Adjusted P-value)")
plt.title("Volcano Plot")
plt.legend()
plt.savefig("volcano_plot.png", dpi=300)

MA Plot

Show fold change vs mean expression:

plt.figure(figsize=(10, 6))

plt.scatter(
    np.log10(results.loc[~significant, "baseMean"] + 1),
    results.loc[~significant, "log2FoldChange"],
    alpha=0.3, s=10, c='gray'
)
plt.scatter(
    np.log10(results.loc[significant, "baseMean"] + 1),
    results.loc[significant, "log2FoldChange"],
    alpha=0.6, s=10, c='red'
)

plt.axhline(0, color='blue', linestyle='--', alpha=0.5)
plt.xlabel("Log10(Base Mean + 1)")
plt.ylabel("Log2 Fold Change")
plt.title("MA Plot")
plt.savefig("ma_plot.png", dpi=300)

Troubleshooting Common Issues

Data Format Problems

Issue: "Index mismatch between counts and metadata"

Solution: Ensure sample names match exactly

print("Counts samples:", counts_df.index.tolist())
print("Metadata samples:", metadata.index.tolist())

# Take intersection if needed
common = counts_df.index.intersection(metadata.index)
counts_df = counts_df.loc[common]
metadata = metadata.loc[common]

Issue: "All genes have zero counts"

Solution: Check if data needs transposition

print(f"Counts shape: {counts_df.shape}")
# If genes > samples, transpose is needed
if counts_df.shape[1] < counts_df.shape[0]:
    counts_df = counts_df.T

Design Matrix Issues

Issue: "Design matrix is not full rank"

Cause: Confounded variables (e.g., all treated samples in one batch)

Solution: Remove confounded variable or add interaction term

# Check confounding
print(pd.crosstab(metadata.condition, metadata.batch))

# Either simplify design or add interaction
design = "~condition"  # Remove batch
# OR
design = "~condition + batch + condition:batch"  # Model interaction

No Significant Genes

Diagnostics:

# Check dispersion distribution
plt.hist(dds.var["dispersions"], bins=50)
plt.show()

# Check size factors
print(dds.obs["size_factors"])

# Look at top genes by raw p-value
print(ds.results_df.nsmallest(20, "pvalue"))

Possible causes:

• Small effect sizes
• High biological variability
• Insufficient sample size
• Technical issues (batch effects, outliers)

Reference Documentation

For comprehensive details beyond this workflow-oriented guide:

• API Reference (references/api_reference.md): Complete documentation of PyDESeq2 classes, methods, and data structures. Use when needing detailed parameter information or understanding object attributes.

• Workflow Guide (references/workflow_guide.md): In-depth guide covering complete analysis workflows, data loading patterns, multi-factor designs, troubleshooting, and best practices. Use when handling complex experimental designs or encountering issues.

Load these references into context when users need:

• Detailed API documentation: Read references/api_reference.md
• Comprehensive workflow examples: Read references/workflow_guide.md
• Troubleshooting guidance: Read references/workflow_guide.md (see Troubleshooting section)

Key Reminders

1. Data orientation matters: Count matrices typically load as genes × samples but need to be samples × genes. Always transpose with .T if needed.

2. Sample filtering: Remove samples with missing metadata before analysis to avoid errors.

3. Gene filtering: Filter low-count genes (e.g., < 10 total reads) to improve power and reduce computational time.

4. Design formula order: Put adjustment variables before the variable of interest (e.g., "~batch + condition" not "~condition + batch").

5. LFC shrinkage timing: Apply shrinkage after statistical testing and only for visualization/ranking purposes. P-values remain based on unshrunken estimates.

6. Result interpretation: Use padj < 0.05 for significance, not raw p-values. The Benjamini-Hochberg procedure controls false discovery rate.

7. Contrast specification: The format is [variable, test_level, reference_level] where test_level is compared against reference_level.

8. Save intermediate objects: Prefer dds.to_picklable_anndata().write_h5ad("dds_result.h5ad") for portable outputs. Only load pickle files that you created yourself and trust.

Installation and Requirements

uv pip install pydeseq2==0.5.4

System requirements:

• Python 3.11+
• PyDESeq2 0.5.4
• pandas 2.2.0+
• numpy 2.0.0+
• scipy 1.12.0+
• scikit-learn 1.4.0+
• anndata 0.11.0+
• formulaic 1.0.2+ and formulaic-contrasts 0.2.0+

Optional for visualization:

• matplotlib
• seaborn

Additional Resources

• Official Documentation: https://pydeseq2.readthedocs.io
• GitHub Repository: https://github.com/scverse/PyDESeq2
• Publication: Muzellec et al. (2023) Bioinformatics, DOI: 10.1093/bioinformatics/btad547
• Original DESeq2 (R): Love et al. (2014) Genome Biology, DOI: 10.1186/s13059-014-0550-8