neo4j-ipycytoscape

This repo demonstrates how to use the ipycytoscape widget to visualize a Neo4j subgraph with cytoscape.js in Jupyter Lab. It also demonstrates how to setup a local instance of the Neo4j graph database and launch Neo4j Browser from a Notebook.

Example subgraph rendered in Jupyter Notebook.

Colors and styles of nodes and relationships can be changed. By clicking on a node, a tooltip with the node properties is displayed. The layout can also be manually changed by dragging nodes to new positions.

Notebook	Description
Neo4j_Example1	Creates a small Neo4j graph locally and demonstrates how to display a Neo4j subgraph
Neo4j_Example2	Accesses the COVID-19-Net Knowledge Graph server and demonstrates how to display a Neo4j subgraph

Run Jupyter Lab locally

Follow the steps below to setup and run the example notebooks on Mac, Linux, or Windows.

Prerequisites: Miniconda3 (light-weight, preferred) or Anaconda3 and Mamba (faster than Conda)

Install Miniconda3
Update an existing miniconda3 installation: conda update conda
Install Mamba: conda install mamba -n base -c conda-forge
Install Git (if not installed): conda install git -n base -c anaconda

1. Clone this Git repository

git clone https://github.com/sbl-sdsc/neo4j-ipycytoscape.git
cd neo4j-ipycytoscape

2. Create a Conda environment

The file environment.yml specifies the Python version and all packages required by the tutorial.

mamba env create -f environment.yml

Activate the conda environment

conda activate neo4j-ipycytoscape

3. Launch Jupyter Lab

jupyter lab

Navigate to the notebooks directory to run the example Jupyter Notebooks.

4. Deactivate the Conda environment

When you are finished, deactivate the conda environment or close the terminal window.

conda deactivate

To remove the Conda environment, run conda env remove -n neo4j-ipycytoscape

Run Jupyter Lab on SDSC Expanse

Expanse is an National Science Foundation (NSF)-funded supercomputer at the San Diego Supercomputer Center at UC San Diego. Free allocations are available for academic researchers. A trial allocation can be obtained by contacting consult@sdsc.edu or a full allocation by applying through the ACCESS progam.

Log into the Expanse Portal with your ACCESS credentials.
Open a Terminal Window ("Shell Access") through the Expanse Portal.
Clone the Git repository neo4j-ipycytoscape

git clone https://github.com/sbl-sdsc/neo4j-ipycytoscape.git

Change into the neo4j-ipycytoscape directory

cd neo4j-ipycytoscape

Launch Jupyter Lab using the Galyleo

Fill in the your project account number in the command below. Galyleo will generate a URL for your Jupyter Lab session and create a Conda environment specified by the environment.yml file.

galyleo launch --account <account_number> --partition shared --cpus 1 --memory 8 --time-limit 00:30:00 --conda-env neo4j-ipycytoscape --conda-yml environment.yml --mamba

Note, before you use Galyleo the first time, append export PATH="/cm/shared/apps/sdsc/galyleo:${PATH}" to the .bashrc file in your home directory then and run source ./.bashrc.

Open a new tab in your web browser and paste the Jupyter Lab URL.

Wait until Jupyter Lab launches. This may take several minutes.

You should see the Satellite Reserver Proxy Servive page launch in your browser.

Run the example notebooks

Navigate to the notebooks directory to run the example Jupyter Notebooks.
Shutdown Jupyter Lab

From the file menu select Shutdown to terminate the process.

If you do not shutdown Jupyter Lab, the process will continue running and use up allocated resources.

Citation

Peter W. Rose, David Valentine, Ilya Zaslavsky, COVID-19-Net: Integrating Health, Pathogen and Environmental Data into a Knowledge Graph for Case Tracking, Analysis, and Forecasting. Available online: https://github.com/covid-19-net/covid-19-community (2020).

Funding

The Neo4j graph visualization for ipycytoscape was in part supported by the National Science Foundation under Award Numbers:

NSF Convergence Accelerator Phase I (RAISE): Knowledge Open Network Queries for Research (KONQUER) (1937136)

NSF RAPID: COVID-19-Net: Integrating Health, Pathogen and Environmental Data into a Knowledge Graph for Case Tracking, Analysis, and Forecasting (2028411)