RegDiffusion FAQ: single-cell gene regulatory network inference#
What is RegDiffusion?#
RegDiffusion is an open-source Python package that uses probabilistic diffusion models to infer gene regulatory networks (GRNs) from single-cell RNA-seq gene expression data. Training does not require a ground-truth network. The package also provides network evaluation, export, and visualization tools.
The method is described in Zhu and Slonim (2024), From Noise to Knowledge.
What input data does RegDiffusion need?#
The Python RegDiffusionTrainer API expects a log-transformed expression
matrix with cells as rows and genes as columns. It accepts NumPy arrays and
SciPy sparse matrices, including a suitably preprocessed adata.X. Remove
genes that are not expressed before training, and preserve the column order
when providing gene names for the inferred network.
The command-line interface instead accepts raw counts in CSV or H5AD files and applies the log transformation itself. Do not pass already log-transformed data to the CLI. See the quick tour and API reference for Python usage.
Does RegDiffusion require a GPU?#
No. Set device='cpu' in RegDiffusionTrainer to use a CPU. A CUDA-capable
NVIDIA GPU accelerates training. The project reports under five minutes on an
A100 GPU and roughly three hours on a 12-core CPU for a 15,000-gene network;
these are example timings, not guarantees for every dataset or configuration.
Can RegDiffusion handle large or sparse single-cell datasets?#
Yes. Sparse expression input avoids materializing the entire normalized
cells-by-genes matrix. The inferred gene-by-gene adjacency matrix is still
dense, so its memory cost grows quadratically with the number of genes.
Set memory_efficient=True to reduce training memory use. The
large-network guide reports benchmark conditions, memory
measurements, and gene-filtering options.
Can I use RegDiffusion with pySCENIC?#
Yes. RegDiffusion can supply inferred network edges for downstream pySCENIC analysis. Follow the pySCENIC tutorial for the workflow. RegDiffusion performs network inference; downstream motif analysis and regulon activity scoring remain separate steps.
How does RegDiffusion relate to GENIE3 and GRNBoost2?#
All three methods address gene regulatory network inference from expression
data. RegDiffusion uses probabilistic diffusion models. For a method choice,
consider your dataset, hardware, runtime budget, and evaluation against a
relevant reference network. The RegDiffusion paper
provides the method’s evaluation; the package includes a GRNEvaluator and
BEELINE dataset loaders for your own comparisons.
Are inferred edges experimentally validated regulatory interactions?#
An inferred edge is a model prediction for follow-up analysis. It does not by itself establish causality or experimental validation. Use reference networks, biological context, and independent experiments to assess specific edges.
How should I cite RegDiffusion?#
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 GitHub repository includes a
CITATION.cff file for citation tools.