---
name: pydeseq2
description: "สกิลสำหรับ Pydeseq2 ใช้เมื่อต้องการแนวทาง ขั้นตอน Template หรือคำแนะนำเชิงปฏิบัติสำหรับงานวิเคราะห์ข้อมูล วิจัย AI ฐานข้อมูล วิทยาศาสตร์ข้อมูล และรายงานเชิงลึก ในหัวข้อนี้"
---

# 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 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:

```python
import pandas as pd
from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

# Pydeseq2
counts_df = pd.read_CSV("counts.CSV", index_col=0).T  # Transpose to samples × genes
metadata = pd.read_CSV("metadata.CSV", index_col=0)

# Pydeseq2
genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]

# Pydeseq2
dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True
)
dds.deseq2()

# Pydeseq2
ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()

# Pydeseq2
results = ds.results_df
significant = results[results.padj < 0.05]
print(f"Found {len(significant)} significant genes")
```

## Core Workflow Steps

### Step 1: Data Preparation

**Input requirements:**
- **Count matrix:** Samples × genes DataFrame with non-negative integer read counts
- **Metadata:** Samples × variables DataFrame with experimental factors

**Common data loading patterns:**

```python
# Pydeseq2
counts_df = pd.read_CSV("counts.CSV", index_col=0).T
metadata = pd.read_CSV("metadata.CSV", index_col=0)

# Pydeseq2
counts_df = pd.read_CSV("counts.tsv", sep="\t", index_col=0).T

# Pydeseq2
import anndata as ad
adata = ad.read_h5ad("data.h5ad")
counts_df = pd.DataFrame(adata.X, index=adata.obs_names, columns=adata.var_names)
metadata = adata.obs
```

**Data filtering:**

```python
# Pydeseq2
genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]

# Pydeseq2
samples_to_keep = ~metadata.condition.isna()
counts_df = counts_df.loc[samples_to_keep]
metadata = metadata.loc[samples_to_keep]
```

### Step 2: Design Specification

The design formula specifies how gene expression is modeled.

**Single-factor designs:**
```python
design = "~condition"  # Simple two-group comparison
```

**Multi-factor designs:**
```python
design = "~batch + condition"  # Control for batch effects
design = "~age + condition"     # Include continuous covariate
design = "~group + condition + group:condition"  # Interaction effects
```

**Design formula guidelines:**
- Use Wilkinson formula notation (R-style)
- Put adjustment variables (e.g., batch) before the main variable of interest
- Ensure variables exist as columns in the metadata DataFrame
- Use appropriate data types (categorical for discrete variables)

### Step 3: DESeq2 Fitting

Initialize the DeseqDataSet and run the complete pipeline:

```python
from pydeseq2.dds import DeseqDataSet

dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True,  # Refit after removing outliers
    n_cpus=1           # Parallel processing (adjust as needed)
)

# Pydeseq2
dds.deseq2()
```

**What `deseq2()` does:**
1. Computes size factors (normalization)
2. Fits genewise dispersions
3. Fits dispersion trend curve
4. Computes dispersion priors
5. Fits MAP dispersions (shrinkage)
6. Fits log fold changes
7. Calculates Cook's distances (outlier detection)
8. Refits if outliers detected (optional)

### Step 4: Statistical Testing

Perform Wald tests to identify differentially expressed genes:

```python
from pydeseq2.ds import DeseqStats

ds = DeseqStats(
    dds,
    contrast=["condition", "treated", "control"],  # Test treated vs control
    alpha=0.05,                # Significance threshold
    cooks_filter=True,         # Filter outliers
    independent_filter=True    # Filter low-power tests
)

ds.summary()
```

**Contrast specification:**
- Format: `[variable, test_level, reference_level]`
- Example: `["condition", "treated", "control"]` tests treated vs control
- If `None`, uses the last coefficient in the design

**Result DataFrame columns:**
- `baseMean`: Mean normalized count across samples
- `log2FoldChange`: Log2 fold change between conditions
- `lfcSE`: Standard error of LFC
- `stat`: Wald test statistic
- `pvalue`: Raw p-value
- `padj`: Adjusted p-value (FDR-corrected via Benjamini-Hochberg)

### Step 5: Optional LFC Shrinkage

Apply shrinkage to reduce noise in fold change estimates:

```python
ds.lfc_shrink()  # Applies apeGLM shrinkage
```

**When to use LFC shrinkage:**
- For visualization (volcano plots, heatmaps)
- For ranking genes by effect size
- When prioritizing genes for follow-up experiments

**Important:** Shrinkage affects only the log2FoldChange values, not the statistical test results (p-values remain unchanged). Use shrunk values for visualization but report unshrunken p-values for significance.

### Step 6: Result Export

Save results and intermediate objects:

```python
import pickle

# Pydeseq2
ds.results_df.to_CSV("deseq2_results.CSV")

# Pydeseq2
significant = ds.results_df[ds.results_df.padj < 0.05]
significant.to_CSV("significant_genes.CSV")

# Pydeseq2
with open("dds_result.pkl", "wb") as f:
    pickle.dump(dds.to_picklable_anndata(), f)
```

## Common Analysis Patterns

### Two-Group Comparison

Standard case-control comparison:

```python
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition")
dds.deseq2()

ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()

results = ds.results_df
significant = results[results.padj < 0.05]
```

### Multiple Comparisons

Testing multiple treatment groups against control:

```python
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~condition")
dds.deseq2()

treatments = ["treatment_A", "treatment_B", "treatment_C"]
all_results = {}

for treatment in treatments:
    ds = DeseqStats(dds, contrast=["condition", treatment, "control"])
    ds.summary()
    all_results[treatment] = ds.results_df

    sig_count = len(ds.results_df[ds.results_df.padj < 0.05])
    print(f"{treatment}: {sig_count} significant genes")
```

### Accounting for Batch Effects

Control for technical variation:

```python
# Pydeseq2
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~batch + condition")
dds.deseq2()

# Pydeseq2
ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()
```

### Continuous Covariates

Include continuous variables like age or dosage:

```python
# Pydeseq2
metadata["age"] = pd.to_numeric(metadata["age"])

dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~age + condition")
dds.deseq2()

ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()
```

## Using the Analysis Script

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

```bash
# Pydeseq2
python scripts/run_deseq2_analysis.py \
  --counts counts.CSV \
  --metadata metadata.CSV \
  --design "~condition" \
  --contrast condition treated control \
  --output results/

# Pydeseq2
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 \
  --plots
```

**Script features:**
- Automatic data loading and validation
- Gene and sample filtering
- Complete DESeq2 pipeline execution
- Statistical testing with customizable parameters
- Result export (CSV, pickle)
- 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

```python
# Pydeseq2
significant = ds.results_df[ds.results_df.padj < 0.05]

# Pydeseq2
sig_and_large = ds.results_df[
    (ds.results_df.padj < 0.05) &
    (abs(ds.results_df.log2FoldChange) > 1)
]

# Pydeseq2
upregulated = significant[significant.log2FoldChange > 0]
downregulated = significant[significant.log2FoldChange < 0]

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

### Ranking and Sorting

```python
# Pydeseq2
top_by_padj = ds.results_df.sort_values("padj").head(20)

# Pydeseq2
ds.lfc_shrink()
ds.results_df["abs_lfc"] = abs(ds.results_df.log2FoldChange)
top_by_lfc = ds.results_df.sort_values("abs_lfc", ascending=False).head(20)

# Pydeseq2
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

```python
# Pydeseq2
print("Size factors:", dds.obsm["size_factors"])

# Pydeseq2
import matplotlib.pyplot as plt
plt.hist(dds.varm["dispersions"], bins=50)
plt.xlabel("Dispersion")
plt.ylabel("Frequency")
plt.title("Dispersion Distribution")
plt.show()

# Pydeseq2
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:

```python
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:

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

# Pydeseq2
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
```python
print(f"Counts shape: {counts_df.shape}")
# Pydeseq2
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
```python
# Pydeseq2
print(pd.crosstab(metadata.condition, metadata.batch))

# Pydeseq2
design = "~condition"  # Remove batch
# Pydeseq2
design = "~condition + batch + condition:batch"  # Model interaction
```

### No Significant Genes

**Diagnostics:**
```python
# Pydeseq2
plt.hist(dds.varm["dispersions"], bins=50)
plt.show()

# Pydeseq2
print(dds.obsm["size_factors"])

# Pydeseq2
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:** Use pickle to save DeseqDataSet objects for later use or additional analyses without re-running the expensive fitting step.

## Installation and Requirements

```bash
uv pip install pydeseq2
```

**System requirements:**
- Python 3.10-3.11
- pandas 1.4.3+
- numpy 1.23.0+
- scipy 1.11.0+
- scikit-learn 1.1.1+
- anndata 0.8.0+

**Optional for visualization:**
- matplotlib
- seaborn

## Additional Resources

- **Official Documentation:** https://pydeseq2.readthedocs.io
- **GitHub Repository:** https://github.com/owkin/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
