scMagnify

scMagnify is a computational framework to infer GRNs and explore dynamic regulation synergy from single-cell multiome data.

🔑scMagnify’s key applications

1. Infer multi-scale dynamic GRNs via nonlinear Granger causality, enabling the identification of key regulators and quantification of their regulation lags.

2. Decompose GRNs into combinatorial regulatory modules (RegFactors) via tensor decomposition.

3. Estimate regulatory activity for TFs and RegFactors via decoupler.

4. Map signaling-to-transcription cascades linking microenvironment cues to intracellular regulation.

🚀Getting started

Please refer to the documentation, in particular, the API documentation.

Cell State Transition
Analysis

notebooks/100_cell_state_analysis.html

TF Binding Network Construction

notebooks/200_tf_binding_network_construct.html

Multi-scale Regulation Inference

notebooks/300_regulation_inference.html

RegFactor Decomposition

notebooks/400_regfactor_decomposition.html

Intracellular Communication

notebooks/500_intracellular_cci.html

⚙️Advanced Usages

📦Installation

You need to have Python 3.10 or newer installed on your system. If you don’t have Python installed, we recommend installing uv.

There are several alternative options to install scMagnify:

1. Install the latest release of scMagnify from PyPI:

uv pip install scmagnify

2. Install the latest stable version from conda-forge using mamba or conda

mamba create -n=scm conda-forge::scmagnify

3. Install the latest development version:

uv pip install git+https://github.com/xfchen0912/scMagnify.git@main

🏷️Release notes

See the changelog.

📬Contact

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

📓Citation

t.b.a

Important resources

About

Learn more about scmagnify.

About the scMagnify

API

Find a detailed documentation of scmagnify.

API

Tutorials

Check out how to use scmagnify for data analysis.

Tutorials