pydeseq2（pydeseq2 搜索）

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:

import pandas as pd
from pydeseq2.dds import DeseqDataSet
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. Initialize and fit DESeq2
dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True
)
dds.deseq2()

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

# 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:

# 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:

design = "~condition"  # Simple two-group comparison

Multi-factor designs:

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:

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)
)

# 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:

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:

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:

import pickle

# 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 DeseqDataSet for later use
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:

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:

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:

# Include batch in design
dds = DeseqDataSet(counts=counts_df, metadata=metadata, design="~batch + condition")
dds.deseq2()

# Test condition while controlling for batch
ds = DeseqStats(dds, contrast=["condition", "treated", "control"])
ds.summary()

Continuous Covariates

Include continuous variables like age or dosage:

# Ensure continuous variable is numeric
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:

# 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 \
  --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

# 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()
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.obsm["size_factors"])

# Examine dispersion estimates
import matplotlib.pyplot as plt
plt.hist(dds.varm["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.varm["dispersions"], bins=50)
plt.show()

# Check size factors
print(dds.obsm["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: Use pickle to save DeseqDataSet objects for later use or additional analyses without re-running the expensive fitting step.

Installation and Requirements

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