/pydeseq2-differential-expression

name: "pydeseq2-differential-expression"
description: "Bulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts."
license: "CC-BY-4.0"

PyDESeq2 Differential Expression Analysis

Overview

PyDESeq2 is a Python reimplementation of the R DESeq2 package for differential gene expression analysis from bulk RNA-seq count data. It fits negative binomial generalized linear models per gene, estimates dispersion with empirical Bayes shrinkage, and performs Wald tests with Benjamini-Hochberg FDR correction. This skill covers the full pipeline from raw counts to publication-ready result tables and visualizations.

When to Use

• Identifying differentially expressed genes between two or more experimental conditions from bulk RNA-seq
• Performing two-group comparisons (e.g., treated vs control) with proper statistical testing
• Running multi-factor designs that account for batch effects or covariates (e.g., `~batch + condition`)
• Applying log2 fold change shrinkage (apeGLM) for ranking and visualization
• Use `omics-plotting` SKILL after DE for publication-quality plots of differential expression results
• Converting R-based DESeq2 workflows to a pure Python environment
• Integrating DE analysis into larger Python bioinformatics pipelines (e.g., with scanpy, pandas)
• Use **DESeq2** (R/Bioconductor) or **edgeR** instead for the reference R implementations with the broadest method support and community validation

Prerequisites

• **Python packages**: `pydeseq2>=0.4`, `pandas>=1.4`, `numpy>=1.23`, `scipy>=1.11`, `scikit-learn>=1.1`, `anndata>=0.8`
• **Data requirements**: Raw (unnormalized) integer count matrix (samples x genes) + sample metadata DataFrame
• **Environment**: Python 3.10+; optional `matplotlib`, `seaborn` for visualization

pip install pydeseq2 matplotlib seaborn

Workflow

Step 1: Data Loading and Validation

Load the count matrix and metadata. PyDESeq2 expects counts as a samples x genes DataFrame with non-negative integers, and metadata as a samples x variables DataFrame with matching indices.

import pandas as pd

# Load data — typical CSV has genes as rows, samples as columns
counts_raw = pd.read_csv("counts.csv", index_col=0)
metadata = pd.read_csv("metadata.csv", index_col=0)

# Transpose if needed: PyDESeq2 requires samples x genes
if counts_raw.shape[0] > counts_raw.shape[1]:
    counts_df = counts_raw.T  # genes x samples → samples x genes
else:
    counts_df = counts_raw

# Validate alignment
common_samples = counts_df.index.intersection(metadata.index)
counts_df = counts_df.loc[common_samples]
metadata = metadata.loc[common_samples]

print(f"Samples: {counts_df.shape[0]}, Genes: {counts_df.shape[1]}")
print(f"Metadata columns: {list(metadata.columns)}")
print(f"Condition counts:\n{metadata['condition'].value_counts()}")

Step 2: Gene Filtering

Remove lowly expressed genes to improve statistical power and reduce multiple testing burden.

# Filter genes with total counts below threshold
min_total_counts = 10
gene_counts = counts_df.sum(axis=0)
genes_to_keep = gene_counts[gene_counts >= min_total_counts].index
counts_df = counts_df[genes_to_keep]

# Optional: require minimum counts in a minimum number of samples
min_count_per_sample = 5
min_samples = 3
genes_expressed = (counts_df >= min_count_per_sample).sum(axis=0) >= min_samples
counts_df = counts_df.loc[:, genes_expressed]

print(f"Genes after filtering: {counts_df.shape[1]}")

Step 3: DeseqDataSet Initialization and Fitting

Create the DESeq dataset object, specify the design formula, and run the full pipeline (size factor estimation, dispersion estimation, model fitting).

from pydeseq2.dds import DeseqDataSet

dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",   # Wilkinson-style formula
    refit_cooks=True,      # Refit after Cook's outlier removal
    n_cpus=4               # Parallel threads
)

# Run: size factors → dispersions → trend → MAP shrinkage → LFC fitting
dds.deseq2()

# Inspect normalization
print(f"Size factors (first 5): {dds.obsm['size_factors'][:5]}")
print(f"Size factor range: {dds.obsm['size_factors'].min():.2f} - {dds.obsm['size_factors'].max():.2f}")

Step 4: Statistical Testing (Wald Test)

Perform Wald tests to identify differentially expressed genes. Specify the contrast as `[variable, test_level, reference_level]`.

from pydeseq2.ds import DeseqStats

ds = DeseqStats(
    dds,
    contrast=["condition", "treated", "control"],
    alpha=0.05,              # FDR threshold
    cooks_filter=True,       # Filter Cook's outliers
    independent_filter=True  # Independent filtering for power
)

ds.summary()

# Access full results
results = ds.results_df
print(f"Total genes tested: {len(results)}")
print(f"Significant (padj < 0.05): {(results.padj < 0.05).sum()}")

Step 5: LFC Shrinkage (Optional)

Apply apeGLM shrinkage to reduce noise in log2 fold change estimates. Use shrunk values for visualization and ranking, not for significance calls.

# Apply shrinkage — modifies results_df.log2FoldChange in place
ds.lfc_shrink()

# Compare pre/post shrinkage effect
print(f"Max |LFC| after shrinkage: {results.log2FoldChange.abs().max():.2f}")
print(f"Genes with |LFC| > 2: {(results.log2FoldChange.abs() > 2).sum()}")

Step 6: Result Filtering and Export

Filter significant genes and export results for downstream analysis.

import numpy as np

# Significance + effect size filter
significant = results[
    (results.padj < 0.05) &
    (results.log2FoldChange.abs() > 1.0)
].copy()

# Separate up/down-regulated
up = significant[significant.log2FoldChange > 0].sort_values("padj")
down = significant[significant.log2FoldChange < 0].sort_values("padj")

print(f"Upregulated