| Name | hpo3 JSON |
| Version |
1.2.0
JSON |
| download |
| home_page | None |
| Summary | A Python package to work with the HPO Ontology using a Rust backend for faster performance |
| upload_time | 2024-03-31 07:44:21 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
hpo
phenotype
genotype
bioinformatics
rare diseases
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://hpo3.readthedocs.io/en/stable/)
[](https://pypi.org/project/hpo3/)
[](https://pypi.org/project/hpo3/)
# HPO3
**hpo3** is a Rust based drop-in replacement of [**PyHPO**](https://pypi.org/project/pyhpo/). It is based on the [**hpo**](https://crates.io/crates/hpo) Rust library which is a performance optimzied implementation of `PyHPO`.
## 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
- 🔬 Enrichment analysis of genes or diseases
**hpo3** 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.
Using the Rust-based [**hpo**](https://crates.io/crates/hpo) library gives super fast performance that allows large analyses. It enables developers to utilize multithreading, further improving performance greatly.
**hpo3** aims to use the exact same API and methods as [PyHPO](https://pypi.org/project/pyhpo/) to allow a very simple replacement for all analysis and statistics methods. However, it does not allow customization and modification of the ontology or individual terms, genes etc.
## Installation
HPO3 is provided as binary wheels for most platforms on [PyPI](https://pypi.org/project/hpo3/), so in most cases you can just run
```bash
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
There are also more examples in the documentation of both [PyHPO](https://pyhpo.readthedocs.io/) and [hpo3](https://hpo3.readthedocs.io/)
```python
from pyhpo import Ontology, HPOSet
# initilize the Ontology
Ontology()
for term in Ontology:
print(f"{term.id} | {term.name}")
# 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 direct parent terms
for p in term.parents:
print(p)
#> HP:0010674 | Abnormality of the curvature of the vertebral column
# Get a list of all ancestor (direct + indirect parent) terms
for p in term.all_parents:
print(p)
#> HP:0000001 | All
#> HP:0011842 | Abnormal skeletal morphology
#> HP:0009121 | Abnormal axial skeleton morphology
#> HP:0033127 | Abnormality of the musculoskeletal system
#> HP:0010674 | Abnormality of the curvature of the vertebral column
#> HP:0000118 | Phenotypic abnormality
#> HP:0000924 | Abnormality of the skeletal system
#> HP:0000925 | Abnormality 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
"""
# Show the categories a term belongs to
for term in Ontology[10049].categories:
print(term)
"""
HP:0033127 | Abnormality of the musculoskeletal system
HP:0040064 | Abnormality of limbs
"""
```
## Documentation
Check out the [hpo3 documentation](https://hpo3.readthedocs.io/en/latest/)
## Parallel processing
**hpo3** is using Rust as backend, so it's able to fully utilize parallel processing. To benefit from this even greater, `hpo3` provides some special helper functions for parallel batch processing in the `helper` submodule
### Similarity scores of HPOSets
Pairwise similarity comparison of `HPOSet`s. Specify a list of comparisons to run and `hpo3` calculates the result using all available CPUs.
Assume you want to compare the clinical information of a patient to the clinical information of 1000s of other patients:
```python
from pyhpo.helper import set_batch_similarity
from pyhpo import Ontology, HPOSet
Ontology()
main_patient = HPOSet.from_queries([
'HP:0002943',
'HP:0008458',
'HP:0100884',
'HP:0002944',
'HP:0002751'
])
# 2 column table with
# - Patient Identifier
# - Comma separated HPO-terms
patient_source = """\
Patient_000001\tHP:0007587,HP:4000044,HP:0001845,HP:0041249,HP:0032648
Patient_000002\tHP:0034338,HP:0031955,HP:0003311,HP:0032564,HP:0100238
Patient_000003\tHP:0031096,HP:0410280,HP:0009899,HP:0002088,HP:0100204
Patient_000004\tHP:0030782,HP:0011439,HP:0009751,HP:0001433,HP:0030336
Patient_000005\tHP:0025029,HP:0033643,HP:0000957,HP:0005593,HP:0012486
Patient_000006\tHP:0009344,HP:0430016,HP:0005621,HP:0010043,HP:0030974
Patient_000007\tHP:0010760,HP:0009331,HP:0100119,HP:0012871,HP:0003653
Patient_000008\tHP:0001636,HP:0000561,HP:0009990,HP:3000075,HP:0007333
Patient_000009\tHP:0011675,HP:0011730,HP:0032729,HP:0032169,HP:0002888
Patient_000010\tHP:0004900,HP:0010761,HP:0020212,HP:0001806,HP:0033372
Patient_000011\tHP:0033336,HP:0025134,HP:0033815,HP:0032290,HP:0032472
Patient_000012\tHP:0004286,HP:0010543,HP:0007258,HP:0009582,HP:0005871
Patient_000013\tHP:0000273,HP:0031967,HP:0033305,HP:0010862,HP:0031750
Patient_000014\tHP:0031403,HP:0020134,HP:0011260,HP:0000826,HP:0030739
Patient_000015\tHP:0009966,HP:0034101,HP:0100736,HP:0032385,HP:0030152
Patient_000016\tHP:0011398,HP:0002165,HP:0000512,HP:0032028,HP:0007807
Patient_000017\tHP:0007465,HP:0031214,HP:0002575,HP:0007765,HP:0100404
Patient_000018\tHP:0033278,HP:0006937,HP:0008726,HP:0012142,HP:0100185
Patient_000019\tHP:0008365,HP:0033377,HP:0032463,HP:0033014,HP:0009338
Patient_000020\tHP:0012431,HP:0004415,HP:0001285,HP:0010747,HP:0008344
Patient_000021\tHP:0008722,HP:0003436,HP:0007313,HP:0031362,HP:0007236
Patient_000022\tHP:0000883,HP:0007542,HP:0012653,HP:0009411,HP:0031773
Patient_000023\tHP:0001083,HP:0030031,HP:0100349,HP:0001120,HP:0010835
Patient_000024\tHP:0410210,HP:0009341,HP:0100811,HP:0032710,HP:0410064
Patient_000025\tHP:0001056,HP:0005561,HP:0003690,HP:0040157,HP:0100059
Patient_000026\tHP:0010651,HP:0500020,HP:0100603,HP:0033443,HP:0008288
Patient_000027\tHP:0012330,HP:0034395,HP:0004066,HP:0000554,HP:0002257
Patient_000028\tHP:0031484,HP:0100423,HP:0030487,HP:0033538,HP:0003172
Patient_000029\tHP:0030901,HP:0025136,HP:0034367,HP:0034101,HP:0045017
Patient_000030\tHP:0100957,HP:0010027,HP:0010806,HP:0020185,HP:0001421
Patient_000031\tHP:0001671,HP:0003885,HP:0001464,HP:0000243,HP:0009549
Patient_000032\tHP:0003521,HP:0003109,HP:0000433,HP:0030647,HP:0100280
Patient_000033\tHP:0006394,HP:0031598,HP:0032199,HP:0010428,HP:0000108
Patient_000034\tHP:0001468,HP:0008689,HP:0410030,HP:0012226,HP:0011388
Patient_000035\tHP:0003536,HP:0001011,HP:0033262,HP:0009978,HP:0025586
Patient_000036\tHP:0031849,HP:0005244,HP:0001664,HP:0041233,HP:0030921
Patient_000037\tHP:0005616,HP:0003874,HP:0011744,HP:0033751,HP:0007971
Patient_000038\tHP:0012836,HP:0033858,HP:0003427,HP:0033880,HP:0030481
Patient_000039\tHP:0100369,HP:0040317,HP:0010561,HP:0010522,HP:0011339
Patient_000040\tHP:0005338,HP:0040179,HP:0004258,HP:0030589,HP:0032981
Patient_000041\tHP:0011758,HP:0033519,HP:0032010,HP:0030710,HP:0010419
Patient_000042\tHP:0002642,HP:0006335,HP:0009895,HP:0001928,HP:0003779
Patient_000043\tHP:0002867,HP:0030404,HP:0033495,HP:0011143,HP:0012642
Patient_000044\tHP:0033432,HP:0005195,HP:0009062,HP:0100617,HP:0033586
Patient_000045\tHP:0011740,HP:0100159,HP:0033480,HP:3000069,HP:0011394
Patient_000046\tHP:0033350,HP:0009840,HP:0040247,HP:0040204,HP:0033099
Patient_000047\tHP:0030323,HP:0032005,HP:0033675,HP:0033869,HP:0010850
Patient_000048\tHP:0003411,HP:0100953,HP:0005532,HP:0032119,HP:0012157
Patient_000049\tHP:0030592,HP:0011691,HP:0010498,HP:0030196,HP:0006414
Patient_000050\tHP:0001549,HP:0040258,HP:0007078,HP:0000657,HP:3000066
"""
comparisons = []
for patient in patient_source.splitlines():
_, terms = patient.split("\t")
comparisons.append(
(
main_patient,
HPOSet.from_queries(terms.split(","))
)
)
similarities = set_batch_similarity(
comparisons,
kind="omim",
method="graphic",
combine="funSimMax"
)
```
(This functionality works well with dataframes, such as `pandas` or `polars`, adding the similarity scores as a new series)
### Gene and Disease enrichments in HPOSets
Calculate the gene enrichment in several HPOSets in parallel
```python
from pyhpo.helper import batch_gene_enrichment
from pyhpo.helper import batch_disease_enrichment
from pyhpo import Ontology, HPOSet
Ontology()
# 2 column table with
# - Patient Identifier
# - Comma separated HPO-terms
patient_source = """\
Patient_000001\tHP:0007587,HP:4000044,HP:0001845,HP:0041249,HP:0032648
Patient_000002\tHP:0034338,HP:0031955,HP:0003311,HP:0032564,HP:0100238
Patient_000003\tHP:0031096,HP:0410280,HP:0009899,HP:0002088,HP:0100204
Patient_000004\tHP:0030782,HP:0011439,HP:0009751,HP:0001433,HP:0030336
Patient_000005\tHP:0025029,HP:0033643,HP:0000957,HP:0005593,HP:0012486
Patient_000006\tHP:0009344,HP:0430016,HP:0005621,HP:0010043,HP:0030974
Patient_000007\tHP:0010760,HP:0009331,HP:0100119,HP:0012871,HP:0003653
Patient_000008\tHP:0001636,HP:0000561,HP:0009990,HP:3000075,HP:0007333
Patient_000009\tHP:0011675,HP:0011730,HP:0032729,HP:0032169,HP:0002888
Patient_000010\tHP:0004900,HP:0010761,HP:0020212,HP:0001806,HP:0033372
Patient_000011\tHP:0033336,HP:0025134,HP:0033815,HP:0032290,HP:0032472
Patient_000012\tHP:0004286,HP:0010543,HP:0007258,HP:0009582,HP:0005871
Patient_000013\tHP:0000273,HP:0031967,HP:0033305,HP:0010862,HP:0031750
Patient_000014\tHP:0031403,HP:0020134,HP:0011260,HP:0000826,HP:0030739
Patient_000015\tHP:0009966,HP:0034101,HP:0100736,HP:0032385,HP:0030152
Patient_000016\tHP:0011398,HP:0002165,HP:0000512,HP:0032028,HP:0007807
Patient_000017\tHP:0007465,HP:0031214,HP:0002575,HP:0007765,HP:0100404
Patient_000018\tHP:0033278,HP:0006937,HP:0008726,HP:0012142,HP:0100185
Patient_000019\tHP:0008365,HP:0033377,HP:0032463,HP:0033014,HP:0009338
Patient_000020\tHP:0012431,HP:0004415,HP:0001285,HP:0010747,HP:0008344
Patient_000021\tHP:0008722,HP:0003436,HP:0007313,HP:0031362,HP:0007236
Patient_000022\tHP:0000883,HP:0007542,HP:0012653,HP:0009411,HP:0031773
Patient_000023\tHP:0001083,HP:0030031,HP:0100349,HP:0001120,HP:0010835
Patient_000024\tHP:0410210,HP:0009341,HP:0100811,HP:0032710,HP:0410064
Patient_000025\tHP:0001056,HP:0005561,HP:0003690,HP:0040157,HP:0100059
Patient_000026\tHP:0010651,HP:0500020,HP:0100603,HP:0033443,HP:0008288
Patient_000027\tHP:0012330,HP:0034395,HP:0004066,HP:0000554,HP:0002257
Patient_000028\tHP:0031484,HP:0100423,HP:0030487,HP:0033538,HP:0003172
Patient_000029\tHP:0030901,HP:0025136,HP:0034367,HP:0034101,HP:0045017
Patient_000030\tHP:0100957,HP:0010027,HP:0010806,HP:0020185,HP:0001421
Patient_000031\tHP:0001671,HP:0003885,HP:0001464,HP:0000243,HP:0009549
Patient_000032\tHP:0003521,HP:0003109,HP:0000433,HP:0030647,HP:0100280
Patient_000033\tHP:0006394,HP:0031598,HP:0032199,HP:0010428,HP:0000108
Patient_000034\tHP:0001468,HP:0008689,HP:0410030,HP:0012226,HP:0011388
Patient_000035\tHP:0003536,HP:0001011,HP:0033262,HP:0009978,HP:0025586
Patient_000036\tHP:0031849,HP:0005244,HP:0001664,HP:0041233,HP:0030921
Patient_000037\tHP:0005616,HP:0003874,HP:0011744,HP:0033751,HP:0007971
Patient_000038\tHP:0012836,HP:0033858,HP:0003427,HP:0033880,HP:0030481
Patient_000039\tHP:0100369,HP:0040317,HP:0010561,HP:0010522,HP:0011339
Patient_000040\tHP:0005338,HP:0040179,HP:0004258,HP:0030589,HP:0032981
Patient_000041\tHP:0011758,HP:0033519,HP:0032010,HP:0030710,HP:0010419
Patient_000042\tHP:0002642,HP:0006335,HP:0009895,HP:0001928,HP:0003779
Patient_000043\tHP:0002867,HP:0030404,HP:0033495,HP:0011143,HP:0012642
Patient_000044\tHP:0033432,HP:0005195,HP:0009062,HP:0100617,HP:0033586
Patient_000045\tHP:0011740,HP:0100159,HP:0033480,HP:3000069,HP:0011394
Patient_000046\tHP:0033350,HP:0009840,HP:0040247,HP:0040204,HP:0033099
Patient_000047\tHP:0030323,HP:0032005,HP:0033675,HP:0033869,HP:0010850
Patient_000048\tHP:0003411,HP:0100953,HP:0005532,HP:0032119,HP:0012157
Patient_000049\tHP:0030592,HP:0011691,HP:0010498,HP:0030196,HP:0006414
Patient_000050\tHP:0001549,HP:0040258,HP:0007078,HP:0000657,HP:3000066
"""
hpo_sets = []
for patient in patient_source.splitlines():
_, terms = patient.split("\t")
hpo_sets.append(HPOSet.from_queries(terms.split(",")))
gene_enrichments = batch_gene_enrichment(hpo_sets)
disease_enrichments = batch_disease_enrichment(hpo_sets)
```
## Development
**hpo3** is completely written in Rust, so you require a stable Rust toolchain:
Rust installation instructions as [on the official website](https://www.rust-lang.org/tools/install):
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
```
Then clone this repository:
```bash
git clone https://github.com/anergictcell/hpo3
cd hpo3
```
Create a Python virtual environment and install maturin:
```bash
virtualenv venv
source venv/bin/activate
pip install maturin
```
And finally build and install the Python library
```bash
maturin develop -r
```
Aaaaand, you're done:
```bash
python
```
```python
from pyhpo import Ontology
Ontology()
for term in Ontology:
print(term.name)
```
Raw data
{
"_id": null,
"home_page": null,
"name": "hpo3",
"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/f4/a5/6edc33b3b8b94f922fca4aa6515ae3d947dbf31f7c3a08ae9049c98d94aa/hpo3-1.2.0.tar.gz",
"platform": null,
"description": "[](https://hpo3.readthedocs.io/en/stable/)\n[](https://pypi.org/project/hpo3/)\n[](https://pypi.org/project/hpo3/)\n\n\n# HPO3\n\n**hpo3** is a Rust based drop-in replacement of [**PyHPO**](https://pypi.org/project/pyhpo/). It is based on the [**hpo**](https://crates.io/crates/hpo) Rust library which is a performance optimzied implementation of `PyHPO`.\n\n\n## Main Features\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- \ud83d\udd2c Enrichment analysis of genes or diseases\n\n**hpo3** 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\nUsing the Rust-based [**hpo**](https://crates.io/crates/hpo) library gives super fast performance that allows large analyses. It enables developers to utilize multithreading, further improving performance greatly.\n\n**hpo3** aims to use the exact same API and methods as [PyHPO](https://pypi.org/project/pyhpo/) to allow a very simple replacement for all analysis and statistics methods. However, it does not allow customization and modification of the ontology or individual terms, genes etc.\n\n\n## Installation\nHPO3 is provided as binary wheels for most platforms on [PyPI](https://pypi.org/project/hpo3/), so in most cases you can just run\n```bash\npip install hpo3\n```\n(For macOS, only Python 3.10 and 3.11 are supported, for both x64 and arm at the moment.)\n\nhpo3 ships with a prebuilt HPO Ontology by default, so you can start right away.\n\n## Examples\n\nThere are also more examples in the documentation of both [PyHPO](https://pyhpo.readthedocs.io/) and [hpo3](https://hpo3.readthedocs.io/)\n\n```python\nfrom pyhpo import Ontology, HPOSet\n\n# initilize the Ontology\nOntology()\n\nfor term in Ontology:\n print(f\"{term.id} | {term.name}\")\n\n# Declare the clinical information of the patients\npatient_1 = HPOSet.from_queries([\n 'HP:0002943',\n 'HP:0008458',\n 'HP:0100884',\n 'HP:0002944',\n 'HP:0002751'\n])\n\npatient_2 = HPOSet.from_queries([\n 'HP:0002650',\n 'HP:0010674',\n 'HP:0000925',\n 'HP:0009121'\n])\n\n# and compare their similarity\npatient_1.similarity(patient_2)\n#> 0.7594183905785477\n\n# Retrieve a term e.g. via its HPO-ID\nterm = Ontology.get_hpo_object('Scoliosis')\n\nprint(term)\n#> HP:0002650 | Scoliosis\n\n# Get information content from Term <--> Omim associations\nterm.information_content['omim']\n#> 2.29\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.)\nlen(term.genes)\n#> 1094\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.)\nlen(term.omim_diseases)\n#> 844\n\n# Get a list of all direct parent terms\nfor 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 ancestor (direct + indirect parent) terms\nfor p in term.all_parents:\n print(p)\n#> HP:0000001 | All\n#> HP:0011842 | Abnormal skeletal morphology\n#> HP:0009121 | Abnormal axial skeleton morphology\n#> HP:0033127 | Abnormality of the musculoskeletal system\n#> HP:0010674 | Abnormality of the curvature of the vertebral column\n#> HP:0000118 | Phenotypic abnormality\n#> HP:0000924 | Abnormality of the skeletal system\n#> HP:0000925 | Abnormality of the vertebral column\n\n# Get a list of all children terms\nfor p in term.children:\n print(p)\n\"\"\"\nHP:0002944 | Thoracolumbar scoliosis\nHP:0008458 | Progressive congenital scoliosis\nHP:0100884 | Compensatory scoliosis\nHP:0002944 | Thoracolumbar scoliosis\nHP:0002751 | Kyphoscoliosis\n\"\"\"\n\n# Show the categories a term belongs to\nfor term in Ontology[10049].categories:\n print(term)\n\"\"\"\nHP:0033127 | Abnormality of the musculoskeletal system\nHP:0040064 | Abnormality of limbs\n\"\"\"\n\n```\n\n\n## Documentation\nCheck out the [hpo3 documentation](https://hpo3.readthedocs.io/en/latest/)\n\n## Parallel processing\n**hpo3** is using Rust as backend, so it's able to fully utilize parallel processing. To benefit from this even greater, `hpo3` provides some special helper functions for parallel batch processing in the `helper` submodule\n\n### Similarity scores of HPOSets\nPairwise similarity comparison of `HPOSet`s. Specify a list of comparisons to run and `hpo3` calculates the result using all available CPUs.\n\nAssume you want to compare the clinical information of a patient to the clinical information of 1000s of other patients:\n\n```python\nfrom pyhpo.helper import set_batch_similarity\nfrom pyhpo import Ontology, HPOSet\n\nOntology()\n\nmain_patient = HPOSet.from_queries([\n 'HP:0002943',\n 'HP:0008458',\n 'HP:0100884',\n 'HP:0002944',\n 'HP:0002751'\n])\n\n# 2 column table with\n# - Patient Identifier\n# - Comma separated HPO-terms\npatient_source = \"\"\"\\\nPatient_000001\\tHP:0007587,HP:4000044,HP:0001845,HP:0041249,HP:0032648\nPatient_000002\\tHP:0034338,HP:0031955,HP:0003311,HP:0032564,HP:0100238\nPatient_000003\\tHP:0031096,HP:0410280,HP:0009899,HP:0002088,HP:0100204\nPatient_000004\\tHP:0030782,HP:0011439,HP:0009751,HP:0001433,HP:0030336\nPatient_000005\\tHP:0025029,HP:0033643,HP:0000957,HP:0005593,HP:0012486\nPatient_000006\\tHP:0009344,HP:0430016,HP:0005621,HP:0010043,HP:0030974\nPatient_000007\\tHP:0010760,HP:0009331,HP:0100119,HP:0012871,HP:0003653\nPatient_000008\\tHP:0001636,HP:0000561,HP:0009990,HP:3000075,HP:0007333\nPatient_000009\\tHP:0011675,HP:0011730,HP:0032729,HP:0032169,HP:0002888\nPatient_000010\\tHP:0004900,HP:0010761,HP:0020212,HP:0001806,HP:0033372\nPatient_000011\\tHP:0033336,HP:0025134,HP:0033815,HP:0032290,HP:0032472\nPatient_000012\\tHP:0004286,HP:0010543,HP:0007258,HP:0009582,HP:0005871\nPatient_000013\\tHP:0000273,HP:0031967,HP:0033305,HP:0010862,HP:0031750\nPatient_000014\\tHP:0031403,HP:0020134,HP:0011260,HP:0000826,HP:0030739\nPatient_000015\\tHP:0009966,HP:0034101,HP:0100736,HP:0032385,HP:0030152\nPatient_000016\\tHP:0011398,HP:0002165,HP:0000512,HP:0032028,HP:0007807\nPatient_000017\\tHP:0007465,HP:0031214,HP:0002575,HP:0007765,HP:0100404\nPatient_000018\\tHP:0033278,HP:0006937,HP:0008726,HP:0012142,HP:0100185\nPatient_000019\\tHP:0008365,HP:0033377,HP:0032463,HP:0033014,HP:0009338\nPatient_000020\\tHP:0012431,HP:0004415,HP:0001285,HP:0010747,HP:0008344\nPatient_000021\\tHP:0008722,HP:0003436,HP:0007313,HP:0031362,HP:0007236\nPatient_000022\\tHP:0000883,HP:0007542,HP:0012653,HP:0009411,HP:0031773\nPatient_000023\\tHP:0001083,HP:0030031,HP:0100349,HP:0001120,HP:0010835\nPatient_000024\\tHP:0410210,HP:0009341,HP:0100811,HP:0032710,HP:0410064\nPatient_000025\\tHP:0001056,HP:0005561,HP:0003690,HP:0040157,HP:0100059\nPatient_000026\\tHP:0010651,HP:0500020,HP:0100603,HP:0033443,HP:0008288\nPatient_000027\\tHP:0012330,HP:0034395,HP:0004066,HP:0000554,HP:0002257\nPatient_000028\\tHP:0031484,HP:0100423,HP:0030487,HP:0033538,HP:0003172\nPatient_000029\\tHP:0030901,HP:0025136,HP:0034367,HP:0034101,HP:0045017\nPatient_000030\\tHP:0100957,HP:0010027,HP:0010806,HP:0020185,HP:0001421\nPatient_000031\\tHP:0001671,HP:0003885,HP:0001464,HP:0000243,HP:0009549\nPatient_000032\\tHP:0003521,HP:0003109,HP:0000433,HP:0030647,HP:0100280\nPatient_000033\\tHP:0006394,HP:0031598,HP:0032199,HP:0010428,HP:0000108\nPatient_000034\\tHP:0001468,HP:0008689,HP:0410030,HP:0012226,HP:0011388\nPatient_000035\\tHP:0003536,HP:0001011,HP:0033262,HP:0009978,HP:0025586\nPatient_000036\\tHP:0031849,HP:0005244,HP:0001664,HP:0041233,HP:0030921\nPatient_000037\\tHP:0005616,HP:0003874,HP:0011744,HP:0033751,HP:0007971\nPatient_000038\\tHP:0012836,HP:0033858,HP:0003427,HP:0033880,HP:0030481\nPatient_000039\\tHP:0100369,HP:0040317,HP:0010561,HP:0010522,HP:0011339\nPatient_000040\\tHP:0005338,HP:0040179,HP:0004258,HP:0030589,HP:0032981\nPatient_000041\\tHP:0011758,HP:0033519,HP:0032010,HP:0030710,HP:0010419\nPatient_000042\\tHP:0002642,HP:0006335,HP:0009895,HP:0001928,HP:0003779\nPatient_000043\\tHP:0002867,HP:0030404,HP:0033495,HP:0011143,HP:0012642\nPatient_000044\\tHP:0033432,HP:0005195,HP:0009062,HP:0100617,HP:0033586\nPatient_000045\\tHP:0011740,HP:0100159,HP:0033480,HP:3000069,HP:0011394\nPatient_000046\\tHP:0033350,HP:0009840,HP:0040247,HP:0040204,HP:0033099\nPatient_000047\\tHP:0030323,HP:0032005,HP:0033675,HP:0033869,HP:0010850\nPatient_000048\\tHP:0003411,HP:0100953,HP:0005532,HP:0032119,HP:0012157\nPatient_000049\\tHP:0030592,HP:0011691,HP:0010498,HP:0030196,HP:0006414\nPatient_000050\\tHP:0001549,HP:0040258,HP:0007078,HP:0000657,HP:3000066\n\"\"\"\n\ncomparisons = []\n\nfor patient in patient_source.splitlines():\n _, terms = patient.split(\"\\t\")\n comparisons.append(\n (\n main_patient,\n HPOSet.from_queries(terms.split(\",\"))\n )\n )\n\nsimilarities = set_batch_similarity(\n comparisons,\n kind=\"omim\",\n method=\"graphic\",\n combine=\"funSimMax\"\n)\n```\n(This functionality works well with dataframes, such as `pandas` or `polars`, adding the similarity scores as a new series)\n\n### Gene and Disease enrichments in HPOSets\n\nCalculate the gene enrichment in several HPOSets in parallel\n\n```python\nfrom pyhpo.helper import batch_gene_enrichment\nfrom pyhpo.helper import batch_disease_enrichment\nfrom pyhpo import Ontology, HPOSet\n\nOntology()\n\n# 2 column table with\n# - Patient Identifier\n# - Comma separated HPO-terms\npatient_source = \"\"\"\\\nPatient_000001\\tHP:0007587,HP:4000044,HP:0001845,HP:0041249,HP:0032648\nPatient_000002\\tHP:0034338,HP:0031955,HP:0003311,HP:0032564,HP:0100238\nPatient_000003\\tHP:0031096,HP:0410280,HP:0009899,HP:0002088,HP:0100204\nPatient_000004\\tHP:0030782,HP:0011439,HP:0009751,HP:0001433,HP:0030336\nPatient_000005\\tHP:0025029,HP:0033643,HP:0000957,HP:0005593,HP:0012486\nPatient_000006\\tHP:0009344,HP:0430016,HP:0005621,HP:0010043,HP:0030974\nPatient_000007\\tHP:0010760,HP:0009331,HP:0100119,HP:0012871,HP:0003653\nPatient_000008\\tHP:0001636,HP:0000561,HP:0009990,HP:3000075,HP:0007333\nPatient_000009\\tHP:0011675,HP:0011730,HP:0032729,HP:0032169,HP:0002888\nPatient_000010\\tHP:0004900,HP:0010761,HP:0020212,HP:0001806,HP:0033372\nPatient_000011\\tHP:0033336,HP:0025134,HP:0033815,HP:0032290,HP:0032472\nPatient_000012\\tHP:0004286,HP:0010543,HP:0007258,HP:0009582,HP:0005871\nPatient_000013\\tHP:0000273,HP:0031967,HP:0033305,HP:0010862,HP:0031750\nPatient_000014\\tHP:0031403,HP:0020134,HP:0011260,HP:0000826,HP:0030739\nPatient_000015\\tHP:0009966,HP:0034101,HP:0100736,HP:0032385,HP:0030152\nPatient_000016\\tHP:0011398,HP:0002165,HP:0000512,HP:0032028,HP:0007807\nPatient_000017\\tHP:0007465,HP:0031214,HP:0002575,HP:0007765,HP:0100404\nPatient_000018\\tHP:0033278,HP:0006937,HP:0008726,HP:0012142,HP:0100185\nPatient_000019\\tHP:0008365,HP:0033377,HP:0032463,HP:0033014,HP:0009338\nPatient_000020\\tHP:0012431,HP:0004415,HP:0001285,HP:0010747,HP:0008344\nPatient_000021\\tHP:0008722,HP:0003436,HP:0007313,HP:0031362,HP:0007236\nPatient_000022\\tHP:0000883,HP:0007542,HP:0012653,HP:0009411,HP:0031773\nPatient_000023\\tHP:0001083,HP:0030031,HP:0100349,HP:0001120,HP:0010835\nPatient_000024\\tHP:0410210,HP:0009341,HP:0100811,HP:0032710,HP:0410064\nPatient_000025\\tHP:0001056,HP:0005561,HP:0003690,HP:0040157,HP:0100059\nPatient_000026\\tHP:0010651,HP:0500020,HP:0100603,HP:0033443,HP:0008288\nPatient_000027\\tHP:0012330,HP:0034395,HP:0004066,HP:0000554,HP:0002257\nPatient_000028\\tHP:0031484,HP:0100423,HP:0030487,HP:0033538,HP:0003172\nPatient_000029\\tHP:0030901,HP:0025136,HP:0034367,HP:0034101,HP:0045017\nPatient_000030\\tHP:0100957,HP:0010027,HP:0010806,HP:0020185,HP:0001421\nPatient_000031\\tHP:0001671,HP:0003885,HP:0001464,HP:0000243,HP:0009549\nPatient_000032\\tHP:0003521,HP:0003109,HP:0000433,HP:0030647,HP:0100280\nPatient_000033\\tHP:0006394,HP:0031598,HP:0032199,HP:0010428,HP:0000108\nPatient_000034\\tHP:0001468,HP:0008689,HP:0410030,HP:0012226,HP:0011388\nPatient_000035\\tHP:0003536,HP:0001011,HP:0033262,HP:0009978,HP:0025586\nPatient_000036\\tHP:0031849,HP:0005244,HP:0001664,HP:0041233,HP:0030921\nPatient_000037\\tHP:0005616,HP:0003874,HP:0011744,HP:0033751,HP:0007971\nPatient_000038\\tHP:0012836,HP:0033858,HP:0003427,HP:0033880,HP:0030481\nPatient_000039\\tHP:0100369,HP:0040317,HP:0010561,HP:0010522,HP:0011339\nPatient_000040\\tHP:0005338,HP:0040179,HP:0004258,HP:0030589,HP:0032981\nPatient_000041\\tHP:0011758,HP:0033519,HP:0032010,HP:0030710,HP:0010419\nPatient_000042\\tHP:0002642,HP:0006335,HP:0009895,HP:0001928,HP:0003779\nPatient_000043\\tHP:0002867,HP:0030404,HP:0033495,HP:0011143,HP:0012642\nPatient_000044\\tHP:0033432,HP:0005195,HP:0009062,HP:0100617,HP:0033586\nPatient_000045\\tHP:0011740,HP:0100159,HP:0033480,HP:3000069,HP:0011394\nPatient_000046\\tHP:0033350,HP:0009840,HP:0040247,HP:0040204,HP:0033099\nPatient_000047\\tHP:0030323,HP:0032005,HP:0033675,HP:0033869,HP:0010850\nPatient_000048\\tHP:0003411,HP:0100953,HP:0005532,HP:0032119,HP:0012157\nPatient_000049\\tHP:0030592,HP:0011691,HP:0010498,HP:0030196,HP:0006414\nPatient_000050\\tHP:0001549,HP:0040258,HP:0007078,HP:0000657,HP:3000066\n\"\"\"\n\nhpo_sets = []\nfor patient in patient_source.splitlines():\n _, terms = patient.split(\"\\t\")\n hpo_sets.append(HPOSet.from_queries(terms.split(\",\")))\n\ngene_enrichments = batch_gene_enrichment(hpo_sets)\ndisease_enrichments = batch_disease_enrichment(hpo_sets)\n```\n\n## Development\n**hpo3** is completely written in Rust, so you require a stable Rust toolchain:\n\nRust installation instructions as [on the official website](https://www.rust-lang.org/tools/install):\n\n```bash\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\n```\n\nThen clone this repository:\n```bash\ngit clone https://github.com/anergictcell/hpo3\ncd hpo3\n```\n\nCreate a Python virtual environment and install maturin:\n```bash\nvirtualenv venv\nsource venv/bin/activate\npip install maturin\n```\n\nAnd finally build and install the Python library\n```bash\nmaturin develop -r\n```\n\nAaaaand, you're done:\n```bash\npython\n```\n\n```python\nfrom pyhpo import Ontology\nOntology()\nfor term in Ontology:\n print(term.name)\n```\n\n",
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