Exploring cancer types with neo4j

How to identify and visualise clusters in knowledge graphs

In this post we’ll identify and visualise different clusters of cancer types by analysing . Here we’re focusing on the disease ontology which shows how different types of diseases relate to each other.

Neo4j is a tool for managing, querying and analysing graph databases. To make it easier to set up we’ll use a docker container.

docker run \
 -it - rm \
 - publish=7474:7474 - publish=7687:7687 \
 - env NEO4J_AUTH=neo4j/123456789 \
 - env NEO4J_PLUGINS='["graph-data-science","apoc","n10s"]' \
 neo4j:5.17.0

In the above command the `-publish` flags set ports to let python query the database directly and let us access it through a browser. The `NEO4J_PLUGINS` argument specifies which plugins to install. Unfortunately, the windows docker image doesn’t seem to be able to handle the installation, so to follow along you’ll need to install neo4j desktop manually. Don’t worry though, the other steps should all still work for you.

While neo4j is running you can access your database by going to http://localhost:7474/ in your browser, or you can use the python driver to connect as below. Note that we’re using the port we published with our docker command above and we’re authenticating with the username and password we also defined above.

URI = "bolt://localhost:7687"
AUTH = ("neo4j", "123456789")
driver = GraphDatabase.driver(URI, auth=AUTH)
driver.verify_connectivity()

Once you have your neo4j database set up, it’s time to get some data. The neo4j plug-in n10s is built to import and handle ontologies; you can use it to embed your data into an existing ontology or to explore the ontology itself. With the cypher commands below we first set some configs to make the results cleaner, then we set up a uniqueness constraint, finally we actually import disease ontology.

CALL n10s.graphconfig.init({ handleVocabUris: "IGNORE" });
CREATE CONSTRAINT n10s_unique_uri FOR (r:Resource) REQUIRE r.uri IS UNIQUE;
CALL n10s.onto.import.fetch(http://purl.obolibrary.org/obo/doid.owl, RDF/XML);

To see how this can be done with the python driver, check out the full code here ). I only had access to this data for a few of the cancer types so I’ve only plotted those nodes. Below we can see that liver cancer does not have an especially high incidence over all. However, incidence rates of liver cancer are much higher than other cancers within its cluster (shown in purple) like oropharynx, larynx, and nasopharynx.

Conclusions

Here we have used the disease ontology to group different cancers into clusters which gives us the context to compare these diseases. Hopefully this little project has shown you how to visually explore an ontology and add that information to your own data.

You can check out the full code for this project at was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.