RegDiffusion: Gene Regulatory Network Inference

RegDiffusion: Gene Regulatory Network Inference#

RegDiffusion is an open-source Python package for gene regulatory network (GRN) inference from single-cell RNA-seq data using probabilistic diffusion models. It learns candidate regulatory relationships from gene expression data without requiring a ground-truth network for training, and includes tools to evaluate, export, and visualize inferred networks.

Start with the Get Started, explore Working with Large Gene Networks, or use inferred edges for Fast SCENIC GRN Analysis with RegDiffusion. The RegDiffusion FAQ: single-cell gene regulatory network inference explains input formats, hardware requirements, and how to interpret the results.

Source code · PyPI package · Research paper

Installation#

regdiffusion is available on PyPI:

pip install regdiffusion

Quick Tour#

This package regdiffusion provides the official implementation of the RegDiffusion algorithm and a set of easy-to-use companion tools to evaluate, analyze, and visualize the inferred network. We also provide access tools to GRN benchmarks and preprocessed single cell datasets for evaluation.

We tried to keep the top level interface straightforward. Right now, it only consists of 4 components: the RegDiffusionTrainer class, the GRN class, the GRNEvaluator class, and the data module.

  • RegDiffusionTrainer: You can use it to train a RegDiffusion model by providing log-transformed expression data in a NumPy array or SciPy sparse matrix, with cells as rows and genes as columns. The training process could be either started or continued using the .train() method. You can export the inferred GRN using the .get_grn() method.

  • GRN: The GRN class provides a container to save the inferred adjacency matrix and the corresponding gene names. You can save the GRN object to a local HDF5 file using the .to_hdf5() method and reload the saved file using the read_hdf5() function. It also comes with functionalities to export or visualize local regions. For example, you can use the .visualize_local_neighborhood() to generate a similar plot as used in the RegDiffusion paper. You can also extract the underlying adjacency list using the .extract_local_neighborhood() method.

  • GRNEvaluator: The ground truth of regulatory relationship often exist as list of edges but the values to be evaluated are often in adjacency matrix. The GRNEvaluator class is designed to fill the gap. Right now it supports common metrics such as AUROC, AUPR, AUPR Ratio, EP, and EPR.

  • data module: Right now, the data module includes quick access to BEELINE benchmarks and our preprocessed single cell datasets on mouse microglia.

Model Structure#

RegDiffusion includes an innovative model structure to estimate the added noise. Here is an high level illustraction. Please refer to our paper for details.

RegDiffusion Structure

Understanding the Inferred Networks#

After the RegDiffusion model converges, what you get is simply an adjacency matrix. When you have thousands or tens of thousands of genes, it’s getting difficult to analyze matrix at that scale. In our paper, we propose a way to analyze the local network by focusing on the genes you care the most. Check out the tutorials on the left side for how to perform a similar network analysis like the one we did in the paper. The Visualizing Inferred GRN tutorial demonstrates interactive network exploration with lightgraph.

Inferred network around ApoE

Inference Speed#

The project reports inference on a 15,000-gene network in under five minutes on an NVIDIA A100 GPU, or roughly three hours on a 12-core CPU. Runtime depends on dataset size, hardware, and training settings. See Working with Large Gene Networks for memory benchmarks and the Get Started for a worked inference example.

Citation#

If you use RegDiffusion, please cite:

Zhu H, Slonim D. From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks. Journal of Computational Biology. 2024;31(11):1087–1103. doi:10.1089/cmb.2024.0607.

The paper describes the method and evaluation. Inferred edges are hypotheses for follow-up analysis, not experimental proof of regulation.

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