hpo-similarity 0.4.3

Testing similarity of HPO terms between probands sharing variants in genes.

Similarity of phenotypes in patient groups

This estimates how likely it is for groups of individuals to have similar phenotypes. To estimate this probability, we need three things:

• a way to quantify phenotypic similarity of two individuals. We use the maximum information content of the most informative common ancestor for each pair of HPO terms from two probands.
• a way to quantify similarity across more than two probands. We sum phenotypic similarity scores from all pairs of probands.
• a null distribution of similarity scores for those probands, generated by randomly sampled groups of probands

The P value is calculated as the proportion of simulated scores greater than the observed probands' score.

Install

Install the package with:

pip install hpo_similarity

Run

hpo_similarity --genes genes.json --phenotypes phenotypes.json

The data directory on github includes example files, one with proband IDs per gene (data/example_genes.json), and the other with HPO terms per proband (data/example_phenotypes.json).

Additional options:

• --output PATH to send output gene and P-values to a file.
• --ontology PATH to use a HPO ontology file other than the default.
• --iterations INTEGER to change the number of iterations (default=100000)

You can also explore the HPO graph using the hpo_similarity package within python, for example:

from hpo_similarity import Ontology

ontology = Ontology()
graph = ontology.get_graph()

# find all descendant terms
graph.get_descendants('HP:0001249')

# get the text for the phenotypic abnormality
graph.nodes['HP:0001249']['name']

Acknowledgments

This code incorporates the following code and datasets:

• a python ontology parser written by Tamás Nepusz.
• the hp.obo file from the Human Phenotype Ontology Consortium.