PELC is a Python package designed to calculate efficiently the HLA Eplet Load (based on the
EpRegistry database) between donors and recipients by loading in a pandas.DataFrame
in eplet_comparison.compute_epletic_load the recipients' and donors' typings. See minimal reproducible example for
more details.
To use pelc, run pip install pelc in your terminal.
Here is a minimal example of how to use pelc to compare two alleles:
from pelc.simple_comparison import simple_comparison
simple_comparison(
"A*68:01",
"A*68:02",
"output", # file will be saved as output.csv in the current directory
verifiedonly=False, # if True, only verified eplets will be considered, otherwise all eplets will be considered
interlocus2=True # doesn't matter for class I alleles
)In the output.csv file created in the current directory, you will find two rows: "In A*68:02 but not in
A*68:01" and "In A*68:01 but not in A*68:02".
Here is a minimal example with the file Template.xlsx (click to download):
import pandas as pd
from pelc import batch_eplet_comp, batch_eplet_comp_aux, output_type
if __name__ == "__main__":
input_path: str = "Template.xlsx"
output_path: str = "MyOutput"
input_df: pd.DataFrame = pd.read_excel(
input_path,
sheet_name="My Sheet",
index_col=0,
)
donordf: pd.DataFrame
recipientdf: pd.DataFrame
donordf, recipientdf = batch_eplet_comp_aux.split_dataframe(input_df)
batch_eplet_comp.compute_epletic_load(
donordf,
recipientdf,
output_path,
output_type.OutputType.DETAILS_AND_COUNT,
class_i=True, # Compute class I eplets comparison?
class_ii=True, # Compute class II eplets comparison?
verifiedonly=False, # How should the epletic charge be computed? Verified eplets only? Or all eplets?
exclude=None, # list of indices to exclude
interlocus2=True # whether or not to take into account interlocus eplets for HLA of class II
)Note that if a typing is unknown, one can use A*, B*, ..., DPB1* as the allele name for both recipients and
donors. If the allele is unknown for only of the two individuals, it is necessary to use A*, B*, ..., DPB1* for
both individuals otherwise the eplet mismatch computation will not be performed for this donor / recipient pair.
If one wants to determine the eplet mismatches between a donor and a recipient but without taking into account
a certain locus, one can use A*, B*, ..., DPB1* as the allele name for both recipients and donors on this locus
and the eplet mismatch computation will only take into account the loci filled in.
If one wants to generate a pandas.DataFrame directly, the output_path argument of simple_comparison can be
set to None. The pandas.DataFrame will be returned by the function. Same goes for compute_epletic_load.
- 55: an eplet did not match the regular expression '^\d+' (ABC, DR, DQ or DP) and it also did not match the regular
expression '^.[PQR]*(\d+)' (interlocus2) either.
Tested on Python 3.10.6 & Python 3.11.1.
platform win32 -- Python 3.10.6, pytest-7.4.0, pluggy-1.2.0
plugins: mypy-0.10.3
collected 39 items
unit_tests_mypy.py .. [ 5%]
unit_tests_simple.py . [ 7%]
pelc\__init__.py . [ 10%]
pelc\_input_sanity_check.py . [ 12%]
pelc\_open_epregistry_databases.py . [ 15%]
pelc\_unexpected_alleles.py . [ 17%]
pelc\batch_eplet_comp.py . [ 20%]
pelc\batch_eplet_comp_aux.py . [ 23%]
pelc\output_type.py . [ 25%]
pelc\simple_comparison.py . [ 28%]
tests\__init__.py . [ 30%]
tests\base_loading_for_tests.py . [ 33%]
tests\test_eplet_mismatches.py ......... [ 56%]
tests\test_extract_key_to_rank_epletes.py .. [ 61%]
tests\test_is_valid_allele.py .. [ 66%]
tests\test_only_one_chromosome.py .. [ 71%]
tests\test_pelc.py .. [ 76%]
tests\test_same_locus.py .. [ 82%]
tests\test_simple_comparison.py ..... [ 94%]
tests\test_unexpected_alleles.py .. [100%]
==================================== mypy =====================================
Success: no issues found in 20 source files
============================= 39 passed in 34.42s =============================
platform win32 -- Python 3.11.1, pytest-7.2.2, pluggy-1.0.0
plugins: anyio-3.6.2, mypy-0.10.3
collected 39 items
unit_tests_mypy.py .. [ 5%]
unit_tests_simple.py . [ 7%]
pelc\__init__.py . [ 10%]
pelc\_input_sanity_check.py . [ 12%]
pelc\_open_epregistry_databases.py . [ 15%]
pelc\_unexpected_alleles.py . [ 17%]
pelc\batch_eplet_comp.py . [ 20%]
pelc\batch_eplet_comp_aux.py . [ 23%]
pelc\output_type.py . [ 25%]
pelc\simple_comparison.py . [ 28%]
tests\__init__.py . [ 30%]
tests\base_loading_for_tests.py . [ 33%]
tests\test_eplet_mismatches.py ......... [ 56%]
tests\test_extract_key_to_rank_epletes.py .. [ 61%]
tests\test_is_valid_allele.py .. [ 66%]
tests\test_only_one_chromosome.py .. [ 71%]
tests\test_pelc.py .. [ 76%]
tests\test_same_locus.py .. [ 82%]
tests\test_simple_comparison.py ..... [ 94%]
tests\test_unexpected_alleles.py .. [100%]
==================================== mypy =====================================
Success: no issues found in 20 source files
============================= 39 passed in 32.56s =============================
- Follows PEP8 Style Guidelines.
- All functions are unit-tested with pytest.
- All variables are correctly type-hinted, reviewed with static type checker
mypy. - All functions are documented with docstrings.
If you use this software, please cite it as below.
- APA:
If you use this software, please cite it as below.
Lhotte, R., Clichet, V., Usureau, C. & Taupin, J. (2022).
Python Eplet Load Calculator (PELC) package (Version 0.5.4.3) [Computer software].
https://doi.org/10.5281/zenodo.7254809
- BibTeX:
@software{lhotte_romain_2022_7526198,
author = {Lhotte, Romain and
Clichet, Valentin and
Usureau, Cédric and
Taupin, Jean-Luc},
title = {Python Eplet Load Calculator},
month = oct,
year = 2022,
publisher = {Zenodo},
version = {0.5.4.3},
doi = {10.5281/zenodo.7526198},
}