Graph Match
Project description
py-graph-match
Matching with Graph
`grma`` is a package for finding HLA matches using graphs approach. The matching is based on grim's imputation.
Pre-requisites
Data Directory Structure
data
├── donors_dir
│ └── donors.txt
├── hpf.csv
└── patients.txt
conf Directory Structure
conf
└── minimal-configuration.json
Follow these steps for finding matches:
Setup a virtual environment (venv) and run:
make install
Quick Getting Started
Get Started with a built-in example.
Build 'Donors Graph'
python test_build_donors_graph.py
Find Matches
Use grma algorthm for finding matches efficiently. You can run the file test_matching.py
python test_matching.py
Find the match results in results
directory.
Full Walk through
Building The Donors' Graph:
The donors' graph is a graph which contains all the donors (the search space). It implemented using a LOL (List of Lists) representation written in cython for better time and memory efficiency. The building might take a lot of memory and time, so it's recommended to save the graph in a pickle file.
Before building the donors' graph, all the donors' HLAs must be imputed using grim
.
Then all the imputation files must be saved under the same directory.
import os
from grma.donorsgraph.build_donors_graph import BuildMatchingGraph
PATH_TO_DONORS_DIR = "data/donors_dir"
PATH_TO_DONORS_GRAPH = "output/donors_graph.pkl"
os.makedirs(f"output", exist_ok=True)
build_matching = BuildMatchingGraph(PATH_TO_DONORS_DIR)
graph = build_matching.graph # access the donors' graph
build_matching.to_pickle(PATH_TO_DONORS_GRAPH) # save the donors' graph to pickle
Search & Match before imputation to patients
The function matching
finds matches up to 3 mismatches and return a pandas.DataFrame
object of the matches sorted by number of mismatches and their score.
The function get these parameters:
- match_graph: a grma donors' graph object -
grma.match.Graph
- grim_config_file: a path to
grim
configuration file
from grma.match import Graph, matching
PATH_TO_DONORS_GRAPH = "output/donors_graph.pkl"
PATH_CONGIF_FILE = "conf/minimal-configuration.json"
# The donors' graph we built earlier
donors_graph = Graph.from_pickle(PATH_TO_DONORS_GRAPH)
# matching_results is a dict - {patient_id: the patient's result dataframe}
matching_results = matching(donors_graph,PATH_CONGIF_FILE, search_id=1, donors_info=[],
threshold=0.1, cutoff=100, save_to_csv=True, output_dir="results")
matching
takes some optional parameters, which you might want to change:
- search_id: An integer identification of the search. default is 0.
- donors_info: An iterable of fields from the database to include in the results. default is None.
- threshold: Minimal score value for a valid match. default is 0.1.
- cutof: Maximum number of matches to return. default is 50.
- verbose: A boolean flag for whether to print the documentation. default is False
- save_to_csv: A boolean flag for whether to save the matching results into a csv file. default is False. If the field is set to True, upon completion of the function, it will generate a directory named
search_1
output_dir
: output directory to write match results file to
Search & Match after imputation to patients
The function find_mathces
find matches up to 3 mismatches and return a pandas.DataFrame
object of the matches
sorted by number of mismatches and their score.
They get these parameters:
- imputation_filename: a path to the file of the patients' typing.
- match_graph: a grma donors' graph object -
grma.match.Graph
from grma.match import Graph, find_matches
PATH_TO_PATIENTS_FILE = "data/patients_file.txt"
PATH_TO_DONORS_GRAPH = "output/donors_graph.pkl"
# The donors' graph we built earlier
donors_graph = Graph.from_pickle(PATH_TO_DONORS_GRAPH)
matching_results = find_matches(PATH_TO_PATIENTS_FILE, donors_graph)
# matching_results is a dict - {patient_id: the patient's result dataframe}
for patient, df in matching_results.items():
# Use here the dataframe 'df' with the results for 'patient'
print(patient, df)
find_matches
takes some optional parameters, which you might want to change:
- search_id: An integer identification of the search. default is 0.
- donors_info: An iterable of fields from the database to include in the results. default is None.
- threshold: Minimal score value for a valid match. default is 0.1.
- cutof: Maximum number of matches to return. default is 50.
- verbose: A boolean flag for whether to print the documentation. default is False
- save_to_csv: A boolean flag for whether to save the matching results into a csv file. default is False.
If the field is set to True, upon completion of the function, it will generate a directory named
Matching_Result s_1
. - calculate_time: A boolean flag for whether to return the matching time for patient. default is False.
In case
calculate_time=True
the output will be dict like this:{patient_id: (results_dataframe, time)}
output_dir
: output directory to write match results file to
Set Database
In order to get in the matching results more information about the donors than the matching information,
one can set a database that has all the donors' information in it.
The database must be a pandas.DataFrame
that its indexes are the donors' IDs.
After setting the database, when calling one of the matching functions,
you may set in the donor_info
variable a list
with the names of the columns you want to join to the result dataframe from the database.
Example of setting the database:
import pandas as pd
from grma.match import set_database
donors = [0, 1, 2]
database = pd.DataFrame([[30], [32], [25]], columns=["Age"], index=donors)
set_database(database)
How to contribute:
- Fork the repository: https://github.com/nmdp-bioinformatics/py-graph-match.git
- Clone the repository locally
git clone https://github.com/<Your-Github-ID>/py-graph-match.git cd py-graph-match
- Make a virtual environment and activate it, run
make venv
> make venv python3 -m venv venv --prompt urban-potato-venv ===================================================================== To activate the new virtual environment, execute the following from your shell source venv/bin/activate
- Source the virtual environment
source venv/bin/activate
- Development workflow is driven through
Makefile
. Usemake
to list show all targets.> make clean remove all build, test, coverage and Python artifacts clean-build remove build artifacts clean-pyc remove Python file artifacts clean-test remove test and coverage artifacts lint check style with flake8 behave run the behave tests, generate and serve report pytest run tests quickly with the default Python test run all(BDD and unit) tests coverage check code coverage quickly with the default Python dist builds source and wheel package docker-build build a docker image for the service docker build a docker image for the service install install the package to the active Python's site-packages venv creates a Python3 virtualenv environment in venv activate activate a virtual environment. Run `make venv` before activating.
- Install all the development dependencies. Will install packages from all
requirements-*.txt
files.make install
- The Gherkin Feature files, step files and pytest files go in
tests
directory:tests |-- features | |-- algorithm | | `-- SLUG\ Match.feature | `-- definition | `-- Class\ I\ HLA\ Alleles.feature |-- steps | |-- HLA_alleles.py | `-- SLUG_match.py `-- unit `-- test_py-graph-match.py
- Package Module files go in the
py-graph-match
directory.py-graph-match |-- __init__.py |-- algorithm | `-- match.py |-- model | |-- allele.py | `-- slug.py `-- py-graph-match.py
- Run all tests with
make test
or different tests withmake behave
ormake pytest
.make behave
will generate report files and open the browser to the report. - Use
python app.py
to run the Flask service app in debug mode. Service will be available at http://localhost:8080/ - Use
make docker-build
to build a docker image using the currentDockerfile
. make docker
will build and run the docker image with the service. Service will be available at http://localhost:8080/
======= History
0.0.1 (2021-08-25)
- First release on PyPI.
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