scMagnify is a versatile Python toolkit for inferring and analyzing gene regulatory networks (GRNs) from single-cell multi-omic datasets
Project description
scMagnify: Multi scAle Gene regulatory Network InFerence and analYsis
scMagnify is a computational framework to infer GRNs and explore dynamic regulation synergy from single-cell multiome data.
🔑scMagnify’s key applications
- Infer
multi-scale dynamic GRNsvia nonlinear Granger causality, enabling the identification of key regulators and quantification of their regulation lags. - Decompose GRNs into combinatorial regulatory modules (
RegFactors) via tensor decomposition. - Estimate
regulatory activityfor TFs and RegFactors via decoupler. - Map signaling-to-transcription cascades linking microenvironment cues to
intracellular regulation.
🚀Getting started
Please refer to the documentation, in particular, the API documentation.
📦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:
- Install the latest release of
scMagnifyfrom PyPI:
uv pip install scmagnify
- Install the latest stable version from conda-forge using mamba or conda
mamba create -n=scm conda-forge::scmagnify
- Install the latest development version:
uv pip install git+https://github.com/LiHongCSBLab/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
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