pymagical 1.1.0

A Python port of the MAGICAL hierarchical Bayesian Gibbs sampler for regulatory circuit inference.

pymagical

pymagical is a high-performance Python port of the MAGICAL (Multiome Accessibility Gene Integration Calling and Looping) algorithm. It provides an automated pipeline for inferring functional regulatory circuits—triads of Transcription Factors (TFs), cis-regulatory elements (Peaks), and target Genes—from paired single-cell RNA-seq and ATAC-seq data.

Key Features

• Fast & Optimized: Significantly faster than the original MATLAB implementation (~1.9x speedup in sampling, up to 15x speedup in data loading).
• Intelligent IO Caching: Automatically caches large sparse matrices and genomic metadata into PyArrow-backed Parquet and NumPy formats for near-instant subsequent loads.
• Biological Directionality: Unlike the original version, pymagical automatically classifies inferred circuits as activators (+) or repressors (-) by analyzing the continuous regression weights calculated during Gibbs sampling.
• HPC Ready: Includes built-in support for high-memory Slurm environments and allows for detailed weight-history dumping for downstream statistical distribution analysis.

Installation

This project uses uv for environment management.

# Clone the repository
git clone <repo-url>
cd pymagical

# Sync the environment
uv sync

Quick Start

1. Command Line Usage

Run the circuit inference directly from your terminal:

# Run with default demo data (astrocytes) for 500 iterations
uv run pymagical --iter 500 --outdir results/

# Run with custom data and dump weight history
uv run pymagical \
    --iter 1000 \
    --prefix my_sample \
    --rna-counts path/to/rna.txt \
    --atac-counts path/to/atac.txt \
    --dump-weights

2. Programmatic Usage

Integrate pymagical into your own Python pipelines:

from pymagical import run_magical

run_magical(
    cand_gene_file="genes.txt",
    cand_peak_file="peaks.txt",
    # ... other file paths ...
    iteration_num=500,
    output_file="my_results.txt"
)

Output Notation

The final triad list includes a biological effect annotation for every identified TF:

TF_Name (Confidence_Probability, Overall_Effect [L_dir, B_dir])

• Overall Effect: + (Activator) or - (Repressor).
• L_dir (Looping): Direction of Peak-to-Gene effect.
• B_dir (Binding): Direction of TF-to-Peak effect.

Example: STAT5B (0.85, + [+,+]) indicates an 85% confident activator that opens a peak which subsequently increases gene expression.

Documentation

• Methodology and Notation Details
• Design Decisions and Benchmarks

Evaluation and Comparison

The eval/ directory contains tools for comparing pymagical against the original MATLAB implementation and profiling performance.

• eval/tests/compare_results.py: Compare fidelity and performance across implementations.
• eval/benchmarks/profile_run.py: Profile the runtime of different execution stages.