Implementation of Characterization of network hierarchy reflects cell-state specificity in genome organization Adapted PyTorch implementation of Poincaré Embeddings for Learning Hierarchical Representations
Simply clone this repository via
git clone https://github.com/wjychem/GGL.git
cd GGL
conda env create -f environment.yml
python setup.py build_ext --inplace
source activate poincare
To fulfill network inference and graph analyses from Hi-C datasets, first
mkdir Data graph_statistics hic
Then run HiC2graph_statistics.m. This will derive the closeness centrality and average shortest path length of chromatin contacts networks (CCNs) as well as the inputs for betweenness centrality calculation and Poincaré Embeddings.
cd train_script/
./sub-train-hic.sh
This will train the embeddings and save the output results in the hic directory with the same name as input Hi-C datasets. Scripts in train_script directory contain the hyperparameter setting to reproduce the results for Characterization of network hierarchy reflects cell-state specificity in genome organization.The loci ids and embedded coordinates will be saved in the output folder as keys.*.txt and pe.coors.*.txt, respectively, which are used for visualization.
- Python 3 with NumPy
- PyTorch
- Scikit-Learn