pydeseq2

What this skill does

# 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:

```python
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

### 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
# From CSV (typical format: genes × samples, needs transpose)
counts_df = pd.read_csv("counts.csv", index_col=0).T
metadata = pd.read_csv("metadata.csv", index_col=0)

# From TSV
counts_df = pd.read_csv("counts.tsv", sep="\t", index_col=0).T

# From AnnData
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
# Remove low-count genes
genes_to_keep = counts_df.columns[counts_df.sum(axis=0) >= 10]
counts_df = counts_df[genes_to_keep]

# Remove samples with missing metadata
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 formulaic/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; continuous variables are detected from the formula, and categorical variables can be forced with `C(variable)` or a pandas categorical dtype
- Do not use deprecated `design_factors`, `continuous_factors`, or `ref_level` arguments in new workflows

### Step 3: DESeq2 Fitting

Initialize the DeseqDataSet and run the complete pipeline:

```python
from pydeseq2.dds import DeseqDataSet
from pydeseq2.default_inference import DefaultInference

inference = DefaultInference(n_cpus=4)
dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True,  # Refit after removing outliers
    inference=inference,
    low_memory=False,
)

# Run the complete DESeq2 pipeline
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
- Use a numeric contrast vector for continuous variables or complex coefficients
- Default contrasts are no longer supported in PyDESeq2 0.5.x; always provide `contrast`

**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(coeff="condition[T.treated]")  # 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
# Export results as CSV
ds.results_df.to_csv("deseq2_results.csv")

# Save significant genes only
significant = ds.results_df[ds.results_df.padj < 0.05]
significant.to_csv("significant_genes.csv")

# Save a portable AnnData object for later inspection
dds.to_picklable_anndata().write_h5ad("dds_result.h5ad")
```

Avoid loading pickle files from untrusted sources. For exchange between agents, pipelines, or collaborators, prefer CSV results and `.h5ad` AnnData files.

## 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
# Include batch in design
dds = DeseqDataSet(counts=count