hpo3 0.2.3

A drop-in replacement for PyHPO using a Rust backend for faster performance

HPO3

HPO3 is a Rust based drop-in replacement of PyHPO. It is based on the hpo Rust library which is a performance optimzied implementation of PyHPO.

Using the Rust-based hpo library increases performance easily 100 fold for many operations. It enables developers to utilize multithreading, further improving performance greatly.

HPO3 aims to use the exact same API and methods as 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.

Current status

Most functionality of PyHPO is present and working, though not extensively tested. If you require correct data and stability, keep using PyHPO. If you need performance and speed for rapid experiments, give HPO3 a try.

Similarity calculations are implemented and working both for single terms and for HPOSets. Hyergeometric enrichment is implemted, but with some small differences compared to PyHPO.

There are some helper functions for parallel batchwise processing, which are amazing if you plan on analyzing large datasets.

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

There are also more examples in the PyHPO documentation:

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

I'm in the process adding proper documentation, but the process is not yet that far. You can check in python by using help(Ontology) or help(methodname) to get some documentation. Otherwise, use the PyHPO documentation for now.

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 HPOSets. 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:

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

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:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Then clone this repository:

git clone https://github.com/anergictcell/hpo3
cd hpo3

Create a Python virtual environment and install maturin:

virtualenv venv
source venv/bin/activate
pip install maturin

And finally build and install the Python library

maturin develop -r

Aaaaand, you're done:

python

from pyhpo import Ontology
Ontology()
for term in Ontology:
    print(term.name)