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:target: https://github.com/anergictcell/pyhpo/actions/workflows/test_coverage.yml/
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.. image:: https://img.shields.io/pypi/v/pyhpo?label=Latest%20Release
:target: https://pypi.org/project/pyhpo/#history
*****
PyHPO
*****
A Python library to work with, analyze, filter and inspect the `Human Phenotype Ontology`_
Visit the `PyHPO Documentation`_ for a more detailed overview of all the functionality.
.. _Human Phenotype Ontology: https://hpo.jax.org/
.. _PyHPO Documentation: https://pyhpo.readthedocs.io/en/latest/
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
**PyHPO** allows working on individual terms ``HPOTerm``, a set of terms ``HPOSet`` and the full ``Ontology``.
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.
Internally the ontology is represented as a branched linked list, every term contains pointers to its parent and child terms. This allows fast tree traversal functionality.
It provides an interface to create ``Pandas Dataframe`` from its data, allowing integration in already existing data anlysis tools.
.. hint::
Check out `hpo3 <https://pypi.org/project/hpo3/>`_ (`Documentation <https://hpo3.readthedocs.io/en/stable/>`_) for an alternative implementation. ``hpo3`` has the exact same functionality, but is much faster 🚀 and supports multithreading for even faster large data processing.
Getting started
===============
The easiest way to install **PyHPO** is via pip
.. code:: bash
pip install pyhpo
This will install a base version of **PyHPO** that offers most functionality.
.. note::
Some features of PyHPO require ``pandas`` and ``scipy``. The standard installation via pip will not include pandas or scipy and PyHPO will work just fine. (You will get a warning on the initial import though).
Without installing ``pandas``, you won't be able to export the Ontology as a ``Dataframe``, everything else will work fine.
Without installing ``scipy``, you won't be able to use the ``stats`` module, especially the enrichment calculations.
If you want to do enrichment analysis, you must also install ``scipy``.
.. code:: bash
pip install 'pyhpo[scipy]'
If you want to work with **PyHPO** using ``pandas`` dataframes, you can install the ``pandas`` dependency
.. code:: bash
pip install 'pyhpo[pandas]'
Or simply install both together:
.. code:: bash
# Include all dependencies
pip install 'pyhpo[all]'
Usage example
=============
Basic use cases
---------------
Some examples for basic functionality of PyHPO
How similar are the phenotypes of two patients
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code:: python
from pyhpo import Ontology
from pyhpo.set import HPOSet
# 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
How close are two HPO terms
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code:: python
from pyhpo import Ontology
# initilize the Ontology ()
_ = Ontology()
term_1 = Ontology.get_hpo_object('Scoliosis')
term_2 = Ontology.get_hpo_object('Abnormal axial skeleton morphology')
path = term_1.path_to_other(term_2)
for t in path[1]:
print(t)
"""
HP:0002650 | Scoliosis
HP:0010674 | Abnormality of the curvature of the vertebral column
HP:0000925 | Abnormality of the vertebral column
HP:0009121 | Abnormal axial skeleton morphology
"""
HPOTerm
-------
An ``HPOTerm`` contains various metadata about the term, as well as pointers to its parents and children terms. You can access its information-content, calculate similarity scores to other terms, find the shortest or longes connection between two terms. List all associated genes or diseases, etc.
Examples:
^^^^^^^^^
Basic functionalities of an HPO-Term
.. code:: python
from pyhpo import Ontology
# initilize the Ontology ()
_ = Ontology()
# 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']
#> 2.39
# Show how many genes are associated to the term
# (Note that this includes indirect associations, associations
# from children terms to genes.)
len(term.genes)
#> 947
# 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)
#> 730
# 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:0002943 | Thoracic scoliosis
HP:0008458 | Progressive congenital scoliosis
HP:0100884 | Compensatory scoliosis
HP:0002944 | Thoracolumbar scoliosis
HP:0002751 | Kyphoscoliosis
"""
*(This script is complete, it should run "as is")*
Some additional functionality, working with more than one term
.. code:: python
from pyhpo import Ontology
_ = Ontology()
term = Ontology.get_hpo_object('Scoliosis')
# Let's get a second term, this time using it HPO-ID
term_2 = Ontology.get_hpo_object('HP:0009121')
print(term_2)
#> HP:0009121 | Abnormal axial skeleton morphology
# Check if the Scoliosis is a direct or indirect child
# of Abnormal axial skeleton morphology
term.child_of(term_2)
#> True
# or vice versa
term_2.parent_of(term)
#> True
# show all nodes between two term:
path = term.path_to_other(term_2)
for t in path[1]:
print(t)
"""
HP:0002650 | Scoliosis
HP:0010674 | Abnormality of the curvature of the vertebral column
HP:0000925 | Abnormality of the vertebral column
HP:0009121 | Abnormal axial skeleton morphology
"""
print(f'Steps from Term 1 to Term 2: {path[0]}')
#> Steps from Term 1 to Term 2: 3
# Calculate the similarity between two terms
term.similarity_score(term_2)
#> 0.442
*(This script is complete, it should run "as is")*
Ontology
--------
The ``Ontology`` contains all HPO terms, their connections to each other and associations to genes and diseases.
It provides some helper functions for ``HPOTerm`` search functionality
Examples
^^^^^^^^
.. code:: python
from pyhpo import Ontology, HPOSet
# initilize the Ontology (this must be done only once)
_ = Ontology()
# Get a term based on its name
term = Ontology.get_hpo_object('Scoliosis')
print(term)
#> HP:0002650 | Scoliosis
# ...or based on HPO-ID
term = Ontology.get_hpo_object('HP:0002650')
print(term)
#> HP:0002650 | Scoliosis
# ...or based on its index
term = Ontology.get_hpo_object(2650)
print(term)
#> HP:0002650 | Scoliosis
# shortcut to retrieve a term based on its index
term = Ontology[2650]
print(term)
#> HP:0002650 | Scoliosis
# Search for term
for term in Ontology.search('olios'):
print(term)
"""
HP:0002211 | White forelock
HP:0002290 | Poliosis
HP:0002650 | Scoliosis
HP:0002751 | Kyphoscoliosis
HP:0002943 | Thoracic scoliosis
HP:0002944 | Thoracolumbar scoliosis
HP:0003423 | Thoracolumbar kyphoscoliosis
HP:0004619 | Lumbar kyphoscoliosis
HP:0004626 | Lumbar scoliosis
HP:0005659 | Thoracic kyphoscoliosis
HP:0008453 | Congenital kyphoscoliosis
HP:0008458 | Progressive congenital scoliosis
HP:0100884 | Compensatory scoliosis
"""
*(This script is complete, it should run "as is")*
The Ontology is a Singleton and should only be initiated once.
It can be reused across several modules, e.g:
``main.py``
.. code:: python
from pyhpo import Ontology, HPOSet
import module2
# initilize the Ontology
_ = Ontology()
if __name__ == '__main__':
module2.find_term('Compensatory scoliosis')
``module2.py``
.. code:: python
from pyhpo import Ontology
def find_term(term):
return Ontology.get_hpo_object(term)
HPOSet
------
An ``HPOSet`` is a collection of ``HPOTerm`` and can be used to represent e.g. a patient's clinical information. It provides APIs for filtering, comparisons to other ``HPOSet`` and term/gene/disease enrichments.
Examples:
^^^^^^^^^
.. code:: python
from pyhpo import Ontology, HPOSet
# initilize the Ontology
_ = Ontology()
# create HPOSets, corresponding to
# e.g. the clinical information of a patient
# You can initiate an HPOSet using either
# - HPO-ID: 'HP:0002943'
# - HPO-Name: 'Scoliosis'
# - HPO-ID (int): 2943
ci_1 = HPOSet.from_queries([
'HP:0002943',
'HP:0008458',
'HP:0100884',
'HP:0002944',
'HP:0002751'
])
ci_2 = HPOSet.from_queries([
'HP:0002650',
'HP:0010674',
'HP:0000925',
'HP:0009121'
])
# Compare the similarity
ci_1.similarity(ci_2)
#> 0.7593552670152157
# Remove all non-leave nodes from a set
ci_leaf = ci_2.child_nodes()
len(ci_2)
#> 4
len(ci_leaf)
#> 1
ci_2
#> HPOSet.from_serialized("925+2650+9121+10674")
ci_leaf
#> HPOSet.from_serialized("2650")
# Check the information content of an HPOSet
ci_1.information_content()
"""
{
'mean': 6.571224974009769,
'total': 32.856124870048845,
'max': 8.97979449089521,
'all': [5.98406221734122, 8.286647310335265, 8.97979449089521, 5.5458072864100645, 4.059813565067086]
}
"""
*(This script is complete, it should run "as is")*
Get genes enriched in an ``HPOSet``
-----------------------------------
Examples:
^^^^^^^^^
.. code:: python
from pyhpo import Ontology, HPOSet
from pyhpo.stats import EnrichmentModel
# initilize the Ontology
_ = Ontology()
ci = HPOSet.from_queries([
'HP:0002943',
'HP:0008458',
'HP:0100884',
'HP:0002944',
'HP:0002751'
])
gene_model = EnrichmentModel('gene')
genes = gene_model.enrichment(method='hypergeom', hposet=ci)
print(genes[0]['item'])
#> PAPSS2
*(This script is complete, it should run "as is")*
For a more detailed description of how to use PyHPO, visit the `PyHPO Documentation <https://pyhpo.readthedocs.io/en/latest/>`_.
Contributing
============
Yes, please do so. We appreciate any help, suggestions for improvement or other feedback. Just create a pull-request or open an issue.
License
=======
PyHPO is released under the `MIT license`_.
PyHPO is using the Human Phenotype Ontology. Find out more at http://www.human-phenotype-ontology.org
Sebastian Köhler, Leigh Carmody, Nicole Vasilevsky, Julius O B Jacobsen, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. (2018) doi: 10.1093/nar/gky1105
.. _MIT license: http://www.opensource.org/licenses/mit-license.php
Raw data
{
"_id": null,
"home_page": "https://github.com/anergictcell/pyhpo",
"name": "pyhpo",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "hpo, phenotype, genotype, bioinformatics, rare diseases",
"author": null,
"author_email": "Jonas Marcello <jonas.marcello@esbme.com>",
"download_url": "https://files.pythonhosted.org/packages/79/39/6d9f139907cc20ac52944298d08ca8bc2a84365f0651d5725f8093885ad4/pyhpo-3.3.1.tar.gz",
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"description": ".. image:: https://codecov.io/gh/anergictcell/pyhpo/branch/master/graph/badge.svg?token=33ZGNQD61W \n :target: https://codecov.io/gh/anergictcell/pyhpo\n\n.. image:: https://github.com/anergictcell/pyhpo/actions/workflows/test_coverage.yml/badge.svg\n :target: https://github.com/anergictcell/pyhpo/actions/workflows/test_coverage.yml/\n\n.. image:: https://img.shields.io/pypi/dm/pyhpo.svg?label=Pypi%20downloads\n :target: https://pypi.org/project/pyhpo/\n\n.. image:: https://img.shields.io/pypi/v/pyhpo?label=Latest%20Release\n :target: https://pypi.org/project/pyhpo/#history\n\n\n*****\nPyHPO\n*****\n\nA Python library to work with, analyze, filter and inspect the `Human Phenotype Ontology`_\n\nVisit the `PyHPO Documentation`_ for a more detailed overview of all the functionality.\n\n.. _Human Phenotype Ontology: https://hpo.jax.org/\n.. _PyHPO Documentation: https://pyhpo.readthedocs.io/en/latest/\n\nMain features\n=============\n\n* \ud83d\udc6b Identify patient cohorts based on clinical features\n* \ud83d\udc68\u200d\ud83d\udc67\u200d\ud83d\udc66 Cluster patients or other clinical information for GWAS\n* \ud83e\ude7b\u2192\ud83e\uddec Phenotype to Genotype studies\n* \ud83c\udf4e\ud83c\udf4a HPO similarity analysis\n* \ud83d\udd78\ufe0f Graph based analysis of phenotypes, genes and diseases\n\n\n**PyHPO** allows working on individual terms ``HPOTerm``, a set of terms ``HPOSet`` and the full ``Ontology``.\n\nThe 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.\n\nInternally the ontology is represented as a branched linked list, every term contains pointers to its parent and child terms. This allows fast tree traversal functionality.\n\nIt provides an interface to create ``Pandas Dataframe`` from its data, allowing integration in already existing data anlysis tools.\n\n.. hint::\n\n Check out `hpo3 <https://pypi.org/project/hpo3/>`_ (`Documentation <https://hpo3.readthedocs.io/en/stable/>`_) for an alternative implementation. ``hpo3`` has the exact same functionality, but is much faster \ud83d\ude80 and supports multithreading for even faster large data processing.\n\nGetting started\n===============\n\nThe easiest way to install **PyHPO** is via pip\n\n.. code:: bash\n\n pip install pyhpo\n\nThis will install a base version of **PyHPO** that offers most functionality.\n\n.. note::\n\n Some features of PyHPO require ``pandas`` and ``scipy``. The standard installation via pip will not include pandas or scipy and PyHPO will work just fine. (You will get a warning on the initial import though).\n\n Without installing ``pandas``, you won't be able to export the Ontology as a ``Dataframe``, everything else will work fine.\n\n Without installing ``scipy``, you won't be able to use the ``stats`` module, especially the enrichment calculations.\n\n\nIf you want to do enrichment analysis, you must also install ``scipy``.\n\n.. code:: bash\n\n pip install 'pyhpo[scipy]'\n\nIf you want to work with **PyHPO** using ``pandas`` dataframes, you can install the ``pandas`` dependency\n\n.. code:: bash\n\n pip install 'pyhpo[pandas]'\n\nOr simply install both together:\n\n.. code:: bash\n\n # Include all dependencies\n pip install 'pyhpo[all]'\n\n\n\nUsage example\n=============\n\nBasic use cases\n---------------\n\nSome examples for basic functionality of PyHPO\n\nHow similar are the phenotypes of two patients\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n from pyhpo import Ontology\n from pyhpo.set import HPOSet\n\n # initilize the Ontology ()\n _ = Ontology()\n\n # Declare the clinical information of the patients\n patient_1 = HPOSet.from_queries([\n 'HP:0002943',\n 'HP:0008458',\n 'HP:0100884',\n 'HP:0002944',\n 'HP:0002751'\n ])\n\n patient_2 = HPOSet.from_queries([\n 'HP:0002650',\n 'HP:0010674',\n 'HP:0000925',\n 'HP:0009121'\n ])\n\n # and compare their similarity\n patient_1.similarity(patient_2)\n #> 0.7594183905785477\n\n\nHow close are two HPO terms\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n from pyhpo import Ontology\n\n # initilize the Ontology ()\n _ = Ontology()\n\n term_1 = Ontology.get_hpo_object('Scoliosis')\n term_2 = Ontology.get_hpo_object('Abnormal axial skeleton morphology')\n\n path = term_1.path_to_other(term_2)\n for t in path[1]:\n print(t)\n\n \"\"\"\n HP:0002650 | Scoliosis\n HP:0010674 | Abnormality of the curvature of the vertebral column\n HP:0000925 | Abnormality of the vertebral column\n HP:0009121 | Abnormal axial skeleton morphology\n \"\"\"\n\n\nHPOTerm\n-------\nAn ``HPOTerm`` contains various metadata about the term, as well as pointers to its parents and children terms. You can access its information-content, calculate similarity scores to other terms, find the shortest or longes connection between two terms. List all associated genes or diseases, etc.\n\nExamples:\n^^^^^^^^^\n\nBasic functionalities of an HPO-Term\n\n.. code:: python\n\n from pyhpo import Ontology\n\n # initilize the Ontology ()\n _ = Ontology()\n\n # Retrieve a term e.g. via its HPO-ID\n term = Ontology.get_hpo_object('Scoliosis')\n\n print(term)\n #> HP:0002650 | Scoliosis\n\n # Get information content from Term <--> Omim associations\n term.information_content['omim']\n #> 2.39\n\n # Show how many genes are associated to the term\n # (Note that this includes indirect associations, associations\n # from children terms to genes.)\n len(term.genes)\n #> 947\n\n # Show how many Omim Diseases are associated to the term\n # (Note that this includes indirect associations, associations\n # from children terms to diseases.)\n len(term.omim_diseases)\n #> 730\n\n # Get a list of all parent terms\n for p in term.parents:\n print(p)\n #> HP:0010674 | Abnormality of the curvature of the vertebral column\n\n # Get a list of all children terms\n for p in term.children:\n print(p)\n \"\"\"\n HP:0002943 | Thoracic scoliosis\n HP:0008458 | Progressive congenital scoliosis\n HP:0100884 | Compensatory scoliosis\n HP:0002944 | Thoracolumbar scoliosis\n HP:0002751 | Kyphoscoliosis\n \"\"\"\n\n*(This script is complete, it should run \"as is\")*\n\n\nSome additional functionality, working with more than one term\n\n.. code:: python\n\n from pyhpo import Ontology\n _ = Ontology()\n term = Ontology.get_hpo_object('Scoliosis')\n\n # Let's get a second term, this time using it HPO-ID\n term_2 = Ontology.get_hpo_object('HP:0009121')\n\n print(term_2)\n #> HP:0009121 | Abnormal axial skeleton morphology\n\n # Check if the Scoliosis is a direct or indirect child\n # of Abnormal axial skeleton morphology\n\n term.child_of(term_2)\n #> True\n\n # or vice versa\n term_2.parent_of(term)\n #> True\n\n # show all nodes between two term:\n path = term.path_to_other(term_2)\n for t in path[1]:\n print(t)\n\n \"\"\"\n HP:0002650 | Scoliosis\n HP:0010674 | Abnormality of the curvature of the vertebral column\n HP:0000925 | Abnormality of the vertebral column\n HP:0009121 | Abnormal axial skeleton morphology\n \"\"\"\n\n print(f'Steps from Term 1 to Term 2: {path[0]}')\n #> Steps from Term 1 to Term 2: 3\n\n\n # Calculate the similarity between two terms\n term.similarity_score(term_2)\n #> 0.442\n\n*(This script is complete, it should run \"as is\")*\n\n\nOntology\n--------\nThe ``Ontology`` contains all HPO terms, their connections to each other and associations to genes and diseases.\nIt provides some helper functions for ``HPOTerm`` search functionality\n\nExamples\n^^^^^^^^\n\n.. code:: python\n\n from pyhpo import Ontology, HPOSet\n\n # initilize the Ontology (this must be done only once)\n _ = Ontology()\n\n # Get a term based on its name\n term = Ontology.get_hpo_object('Scoliosis')\n print(term)\n #> HP:0002650 | Scoliosis\n\n # ...or based on HPO-ID\n term = Ontology.get_hpo_object('HP:0002650')\n print(term)\n #> HP:0002650 | Scoliosis\n\n # ...or based on its index\n term = Ontology.get_hpo_object(2650)\n print(term)\n #> HP:0002650 | Scoliosis\n\n # shortcut to retrieve a term based on its index\n term = Ontology[2650]\n print(term)\n #> HP:0002650 | Scoliosis\n\n # Search for term\n for term in Ontology.search('olios'):\n print(term)\n\n \"\"\"\n HP:0002211 | White forelock\n HP:0002290 | Poliosis\n HP:0002650 | Scoliosis\n HP:0002751 | Kyphoscoliosis\n HP:0002943 | Thoracic scoliosis\n HP:0002944 | Thoracolumbar scoliosis\n HP:0003423 | Thoracolumbar kyphoscoliosis\n HP:0004619 | Lumbar kyphoscoliosis\n HP:0004626 | Lumbar scoliosis\n HP:0005659 | Thoracic kyphoscoliosis\n HP:0008453 | Congenital kyphoscoliosis\n HP:0008458 | Progressive congenital scoliosis\n HP:0100884 | Compensatory scoliosis\n \"\"\"\n\n*(This script is complete, it should run \"as is\")*\n\nThe Ontology is a Singleton and should only be initiated once.\nIt can be reused across several modules, e.g:\n\n``main.py``\n\n.. code:: python\n\n from pyhpo import Ontology, HPOSet\n\n import module2\n\n # initilize the Ontology\n _ = Ontology()\n\n if __name__ == '__main__':\n module2.find_term('Compensatory scoliosis')\n\n\n``module2.py``\n\n.. code:: python\n\n from pyhpo import Ontology\n\n def find_term(term):\n return Ontology.get_hpo_object(term)\n\n\nHPOSet\n------\nAn ``HPOSet`` is a collection of ``HPOTerm`` and can be used to represent e.g. a patient's clinical information. It provides APIs for filtering, comparisons to other ``HPOSet`` and term/gene/disease enrichments.\n\n\nExamples:\n^^^^^^^^^\n\n.. code:: python\n\n from pyhpo import Ontology, HPOSet\n\n # initilize the Ontology\n _ = Ontology()\n\n # create HPOSets, corresponding to \n # e.g. the clinical information of a patient\n # You can initiate an HPOSet using either\n # - HPO-ID: 'HP:0002943'\n # - HPO-Name: 'Scoliosis'\n # - HPO-ID (int): 2943\n\n ci_1 = HPOSet.from_queries([\n 'HP:0002943',\n 'HP:0008458',\n 'HP:0100884',\n 'HP:0002944',\n 'HP:0002751'\n ])\n\n ci_2 = HPOSet.from_queries([\n 'HP:0002650',\n 'HP:0010674',\n 'HP:0000925',\n 'HP:0009121'\n ])\n\n # Compare the similarity\n ci_1.similarity(ci_2)\n #> 0.7593552670152157\n\n # Remove all non-leave nodes from a set\n ci_leaf = ci_2.child_nodes()\n len(ci_2)\n #> 4\n len(ci_leaf)\n #> 1\n ci_2\n #> HPOSet.from_serialized(\"925+2650+9121+10674\")\n ci_leaf\n #> HPOSet.from_serialized(\"2650\")\n\n # Check the information content of an HPOSet\n ci_1.information_content()\n \"\"\"\n {\n 'mean': 6.571224974009769,\n 'total': 32.856124870048845,\n 'max': 8.97979449089521,\n 'all': [5.98406221734122, 8.286647310335265, 8.97979449089521, 5.5458072864100645, 4.059813565067086]\n }\n \"\"\"\n\n*(This script is complete, it should run \"as is\")*\n\n\nGet genes enriched in an ``HPOSet``\n-----------------------------------\n\nExamples:\n^^^^^^^^^\n\n.. code:: python\n\n from pyhpo import Ontology, HPOSet\n from pyhpo.stats import EnrichmentModel\n\n # initilize the Ontology\n _ = Ontology()\n\n ci = HPOSet.from_queries([\n 'HP:0002943',\n 'HP:0008458',\n 'HP:0100884',\n 'HP:0002944',\n 'HP:0002751'\n ])\n\n gene_model = EnrichmentModel('gene')\n genes = gene_model.enrichment(method='hypergeom', hposet=ci)\n \n print(genes[0]['item'])\n #> PAPSS2\n\n*(This script is complete, it should run \"as is\")*\n\n\nFor a more detailed description of how to use PyHPO, visit the `PyHPO Documentation <https://pyhpo.readthedocs.io/en/latest/>`_.\n\n\n\nContributing\n============\n\nYes, please do so. We appreciate any help, suggestions for improvement or other feedback. Just create a pull-request or open an issue.\n\nLicense\n=======\n\nPyHPO is released under the `MIT license`_.\n\n\nPyHPO is using the Human Phenotype Ontology. Find out more at http://www.human-phenotype-ontology.org\n\nSebastian K\u00f6hler, Leigh Carmody, Nicole Vasilevsky, Julius O B Jacobsen, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. (2018) doi: 10.1093/nar/gky1105\n\n.. _MIT license: http://www.opensource.org/licenses/mit-license.php\n\n\n",
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