\(HPO_3\)

Table of Contents

• HPO3

• Installation

• Examples

• Examples for multithreading

• PyHPO vs hpo3

HPO3

\(HPO_3\). is a Python library to work with the Human Phenotype Ontology (HPO). It can calculate similarities between individual terms or between sets of terms. It can also calculate the enrichment of gene or disease associations to a set of HPO terms.

Main features

• 👫 Identify patient cohorts based on clinical features

• 👨‍👧‍👦 Cluster patients or other clinical information for GWAS

• 🩻→🧬 Phenotype to Genotype studies

• 🍎🍊 HPO similarity analysis

• 🕸️ Graph based analysis of phenotypes, genes and diseases

• 🔗 Linkage calculation for dendogram analysis

The library is helpful for discovery of novel gene-disease associations and GWAS data analysis studies. At the same time, it can be used for oragnize clinical information of patients in research or diagnostic settings.

hpo3 aims to be a drop-in replacement for pyhpo, but is written in Rust and thus much much faster. Batchwise operations can also utilize multithreading, increasing the performance even more.

Hint

You can also check out the documentation of pyhpo, the API is almost 100% identical.

hpo3 does have some extra functionality in the Helper functions module.

Installation

hpo3 is provided as binary wheels for most platforms on PyPI, so in most cases you can just run

pip install hpo3

(For macOS, only Python 3.10 and 3.11 are supported, for both x64 and arm at the moment.)

hpo3 ships with a prebuilt HPO Ontology by default, so you can start right away.

Examples

from pyhpo import stats, Ontology, HPOSet, Gene

# initilize the Ontology
Ontology()

# Declare the clinical information of the patients
patient_1 = HPOSet.from_queries([
   'HP:0002943',
   'HP:0008458',
   'HP:0100884',
   'HP:0002944',
   'HP:0002751'
])

patient_2 = HPOSet.from_queries([
   'HP:0002650',
   'HP:0010674',
   'HP:0000925',
   'HP:0009121'
])

# and compare their similarity
patient_1.similarity(patient_2)
#> 0.7594183905785477

# Retrieve a term e.g. via its HPO-ID
term = Ontology.get_hpo_object('Scoliosis')

print(term)
#> HP:0002650 | Scoliosis

# Get information content from Term <--> Omim associations
term.information_content['omim']
#> 2.29

# Show how many genes are associated to the term
# (Note that this includes indirect associations, associations
# from children terms to genes.)
len(term.genes)
#> 1094

# Show how many Omim Diseases are associated to the term
# (Note that this includes indirect associations, associations
# from children terms to diseases.)
len(term.omim_diseases)
#> 844

# Get a list of all parent terms
for p in term.parents:
   print(p)
#> HP:0010674 | Abnormality of the curvature of the vertebral column

# Get a list of all children terms
for p in term.children:
   print(p)
"""
HP:0002944 | Thoracolumbar scoliosis
HP:0008458 | Progressive congenital scoliosis
HP:0100884 | Compensatory scoliosis
HP:0002944 | Thoracolumbar scoliosis
HP:0002751 | Kyphoscoliosis
"""

# Calculate the enrichment of genes in an HPOSet
gene_model = stats.EnrichmentModel('gene')
genes = gene_model.enrichment(method='hypergeom', hposet=patient_1)

print(genes[0])
# >> {'enrichment': 5.453829934109905e-05, 'fold': 33.67884615384615, 'count': 3, 'item': <Gene (PLOD1)>}

Examples for multithreading

hpo3 shines even more when it comes to batchwise operations:

Calculate the pairwise similarity of the HPOSets from all genes

import itertools
from pyhpo import Ontology, HPOSet, helper

Ontology()

gene_sets = [g.hpo_set() for g in Ontology.genes]
gene_set_combinations = [
   (a[0], a[1]) for a in itertools.combinations(gene_sets, 2)
]

similarities = helper.set_batch_similarity(
   gene_set_combinations[0:1000],  # only calculating for for 1000 comparisons to save time
   kind="omim",
   method="graphic",
   combine="funSimAvg"
)

Calculate the similarity of of a patient’s HPO term to all diseases

import itertools
from pyhpo import Ontology, HPOSet, helper

Ontology()

patient_1 = HPOSet.from_queries([
   'HP:0002943',
   'HP:0008458',
   'HP:0100884',
   'HP:0002944',
   'HP:0002751'
])

# casting the gene set to a list to main order for later lookups
genes = list(Ontology.genes)
comparisons = [(patient_1, g.hpo_set()) for g in genes]

similarities = helper.set_batch_similarity(
   comparisons,
   kind="omim",
   method="graphic",
   combine="funSimAvg"
)

# Get most similar gene
top_score = max(similarities)
genes[similarities.index(top_score)]
# >> <Gene (POP1)>

Calculate the disease enrichment for every gene’s HPOSet

import itertools
from pyhpo import Ontology, HPOSet, helper

Ontology()

# casting the gene set to a list to main order for later lookups
genes = list(Ontology.genes)
gene_sets = [g.hpo_set() for g in genes]

enrichments = helper.batch_disease_enrichment(gene_sets)
print(f"The most enriched disease for {genes[0]} is {enrichments[0][0]}")

# >> The most enriched disease for 730 | C7 is {
# >>     'enrichment': 3.6762699175625894e-42,
# >>     'fold': 972.9444444444443,
# >>     'count': 13,
# >>     'item': <OmimDisease (610102)>
# >> }

Or vice versa (genes enriched for every disease)

import itertools
from pyhpo import Ontology, HPOSet, helper

Ontology()

# casting the gene set to a list to main order for later lookups
diseases = list(Ontology.omim_diseases)
disease_sets = [d.hpo_set() for d in diseases]

enrichments = helper.batch_gene_enrichment(disease_sets)
print(f"The most enriched gene for {diseases[0]} is {enrichments[0][0]}")

# >> The most enriched gene for 619510 | Immunodeficiency 85 and autoimmunity is {
# >>     'enrichment': 7.207370728788139e-45,
# >>     'fold': 66.0867924528302,
# >>     'count': 24,
# >>     'item': <Gene (TOM1)>
# >> }