PyDESeq2 API Reference

This document provides comprehensive API reference for PyDESeq2 classes, methods, and utilities.

Core Classes

DeseqDataSet

The main class for differential expression analysis that handles data processing from normalization through log-fold change fitting.

Purpose: Implements dispersion and log fold-change (LFC) estimation for RNA-seq count data.

Initialization Parameters:

• counts: pandas DataFrame of shape (samples × genes) containing non-negative integer read counts
• metadata: pandas DataFrame of shape (samples × variables) with sample annotations
• design: str, Wilkinson formula specifying the statistical model (e.g., "~condition", "~group + condition")
• refit_cooks: bool, whether to refit parameters after removing Cook's distance outliers (default: True)
• n_cpus: int, number of CPUs to use for parallel processing (optional)
• quiet: bool, suppress progress messages (default: False)

Key Methods:

deseq2()

Run the complete DESeq2 pipeline for normalization and dispersion/LFC fitting.

Steps performed:

1. Compute normalization factors (size factors)
2. Fit genewise dispersions
3. Fit dispersion trend curve
4. Calculate dispersion priors
5. Fit MAP (maximum a posteriori) dispersions
6. Fit log fold changes
7. Calculate Cook's distances for outlier detection
8. Optionally refit if refit_cooks=True

Returns: None (modifies object in-place)

to_picklable_anndata()

Convert the DeseqDataSet to an AnnData object that can be saved with pickle.

Returns: AnnData object with:

• X: count data matrix
• obs: sample-level metadata (1D)
• var: gene-level metadata (1D)
• varm: gene-level multi-dimensional data (e.g., LFC estimates)

Usage:

import pickle
with open("result_adata.pkl", "wb") as f:
    pickle.dump(dds.to_picklable_anndata(), f)

Attributes (after running deseq2()):

• layers: dict containing various matrices (normalized counts, etc.)
• varm: dict containing gene-level results (log fold changes, dispersions, etc.)
• obsm: dict containing sample-level information
• uns: dict containing global parameters

DeseqStats

Class for performing statistical tests and computing p-values for differential expression.

Purpose: Facilitates PyDESeq2 statistical tests using Wald tests and optional LFC shrinkage.

Initialization Parameters:

• dds: DeseqDataSet object that has been processed with deseq2()
• contrast: list or None, specifies the contrast for testing
  • Format: [variable, test_level, reference_level]
  • Example: ["condition", "treated", "control"] tests treated vs control
  • If None, uses the last coefficient in the design formula
• alpha: float, significance threshold for independent filtering (default: 0.05)
• cooks_filter: bool, whether to filter outliers based on Cook's distance (default: True)
• independent_filter: bool, whether to perform independent filtering (default: True)
• n_cpus: int, number of CPUs for parallel processing (optional)
• quiet: bool, suppress progress messages (default: False)

Key Methods:

summary()

Run Wald tests and compute p-values and adjusted p-values.

Steps performed:

1. Run Wald statistical tests for specified contrast
2. Optional Cook's distance filtering
3. Optional independent filtering to remove low-power tests
4. Multiple testing correction (Benjamini-Hochberg procedure)

Returns: None (results stored in results_df attribute)

Result DataFrame columns:

• baseMean: mean normalized count across all samples
• log2FoldChange: log2 fold change between conditions
• lfcSE: standard error of the log2 fold change
• stat: Wald test statistic
• pvalue: raw p-value
• padj: adjusted p-value (FDR-corrected)

lfc_shrink(coeff=None)

Apply shrinkage to log fold changes using the apeGLM method.

Purpose: Reduces noise in LFC estimates for better visualization and ranking, especially for genes with low counts or high variability.

Parameters:

• coeff: str or None, coefficient name to shrink (if None, uses the coefficient from the contrast)

Important: Shrinkage is applied only for visualization/ranking purposes. The statistical test results (p-values, adjusted p-values) remain unchanged.

Returns: None (updates results_df with shrunk LFCs)

Attributes:

• results_df: pandas DataFrame containing test results (available after summary())

Utility Functions

pydeseq2.utils.load_example_data(modality="single-factor")

Load synthetic example datasets for testing and tutorials.

Parameters:

• modality: str, either "single-factor" or "multi-factor"

Returns: tuple of (counts_df, metadata_df)

• counts_df: pandas DataFrame with synthetic count data
• metadata_df: pandas DataFrame with sample annotations

Preprocessing Module

The pydeseq2.preprocessing module provides utilities for data preparation.

Common operations:

• Gene filtering based on minimum read counts
• Sample filtering based on metadata criteria
• Data transformation and normalization

Inference Classes

Inference

Abstract base class defining the interface for DESeq2-related inference methods.

DefaultInference

Default implementation of inference methods using scipy, sklearn, and numpy.

Purpose: Provides the mathematical implementations for:

• GLM (Generalized Linear Model) fitting
• Dispersion estimation
• Trend curve fitting
• Statistical testing

Data Structure Requirements

Count Matrix

• Shape: (samples × genes)
• Type: pandas DataFrame
• Values: Non-negative integers (raw read counts)
• Index: Sample identifiers (must match metadata index)
• Columns: Gene identifiers

Metadata

• Shape: (samples × variables)
• Type: pandas DataFrame
• Index: Sample identifiers (must match count matrix index)
• Columns: Experimental factors (e.g., "condition", "batch", "group")
• Values: Categorical or continuous variables used in the design formula

Important Notes

• Sample order must match between counts and metadata
• Missing values in metadata should be handled before analysis
• Gene names should be unique
• Count files often need transposition: counts_df = counts_df.T

Common Workflow Pattern

from pydeseq2.dds import DeseqDataSet
from pydeseq2.ds import DeseqStats

# 1. Initialize dataset
dds = DeseqDataSet(
    counts=counts_df,
    metadata=metadata,
    design="~condition",
    refit_cooks=True
)

# 2. Fit dispersions and LFCs
dds.deseq2()

# 3. Perform statistical testing
ds = DeseqStats(
    dds,
    contrast=["condition", "treated", "control"],
    alpha=0.05
)
ds.summary()

# 4. Optional: Shrink LFCs for visualization
ds.lfc_shrink()

# 5. Access results
results = ds.results_df

Version Compatibility

PyDESeq2 aims to match the default settings of DESeq2 v1.34.0. Some differences may exist as it is a from-scratch reimplementation in Python.

Tested with:

• Python 3.10-3.11
• anndata 0.8.0+
• numpy 1.23.0+
• pandas 1.4.3+
• scikit-learn 1.1.1+
• scipy 1.11.0+