multigedi

Gene Expression Decomposition for Integration

A scverse-compliant Python package for single-cell RNA-seq batch correction and dimensionality reduction.

multigedi implements the GEDI algorithm for integrating single-cell RNA sequencing data across multiple samples and batches. It uses a latent variable model with block coordinate descent optimization to learn shared gene expression patterns while correcting for batch effects.

Quick Install

pip install multigedi

Quick Start

import multigedi as gd
import scanpy as sc

# Load your data
adata = sc.read_h5ad("data.h5ad")

# Run GEDI batch correction
gd.tl.gedi(adata, batch_key="sample", n_latent=10)

# Compute UMAP embedding
gd.tl.umap(adata)

# Visualize
gd.pl.embedding(adata, color=["sample", "cell_type"])

Key Features

  • Memory-efficient: C++ backend keeps large matrices in native memory

  • Fast: OpenMP parallelization for multi-threaded optimization

  • scverse-compliant: Works seamlessly with AnnData, scanpy, and the scverse ecosystem

  • Flexible: Supports multiple input types (counts, log-transformed, binary)

  • Comprehensive: Includes projections, embeddings, imputation, and differential analysis

Documentation

Getting Started

API Reference

Modules

multigedi follows the scanpy convention with submodules for different functionality:

Module

Description

gd.tl

Tools for model training, projections, embeddings, imputation, and analysis

gd.pl

Plotting functions for embeddings, convergence, and feature visualization

gd.io

Input/output for H5AD, 10X formats, and model persistence

Citation

If you use multigedi in your research, please cite:

Mikaeili Namini, A., & Najafabadi, H.S. (2024). GEDI: Gene Expression Decomposition for Integration of single-cell RNA-seq data.

License

multigedi is released under the MIT License.