pydeseq2

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