CellGRN is a framework that shifts the paradigm from global fitting to cell-specific reconstruction. CellGRN adopts a two-step strategy: it first leverages a global regulatory backbone from a global modeling strategy, and subsequently reconstructs cell-specific regulatory weights by projecting individual cell states onto this backbone.
CellGRN-sparse is an accelerated version of CellGRN, which skipped the step of cell-specific GRN output and optimized with sparse matrix and block computation.
All datasets can be downloaded from FigShare or baidu Net Dist(https://pan.baidu.com/s/1mkpBOzyyuOHvmTPC4sMGBg?pwd=4r8v)
- Python >=3.8, scanpy, scikit-learn,pyranges,umap-learn, anndata,pandas, numpy, scipy, jupyter
- Visualization requires extra matplotlib, seaborn and R>=3.5.
If you have not installed python dependencies above, we recommend to use conda in install CellGRN in an independent environment. The conda tool (miniconda) can be installed from anaconda website.
- Step1: create a conda environment.
conda env create -f cellgrn_env.yml -n cellgrn
conda activate cellgrn- Install cellgrn package.
git clone https://github.com/xxx/cellgrn
# set dir to folder
cd cellgrn
pip install .- Test the installation in python
import cellgrnA demo code for CellGRN and CellGRN-sparse tutorial
- code/grn_method: SCENIC+ and LINGER script.
- code/preprocess: Preprocessing scripts for input datasets and validation datasets.
- code/***_run.ipynb: Demo code to run CellGRN in multiome/spatial-multiome datasets in our manuscript.
- eval/***_eval.ipynb: Benchmark pipelines for each dataset.
- eval/visualization/: R scripts to plot figure in our manuscript.
Reconstructing cell-specific GRN to decipher regulatory heterogeneity from single-cell multi-omics. (submited)