Graph Based Multi-Race EM

Pre-requisites

Make a virtual environment and activate it, run make venv

make venv
source venv/bin/activate

Install all the development dependencies. Will install packages from all requirements-*.txt files.

make install

Data Directory Structure

Donor files are in data directory, specified in the conf file.

data
└── ct_mr_don_10.txt

conf Directory Structure

Configure input/output directories and EM specific parameters.

conf
└── minimal-configuration.json

Example EM run

Run the test_em script to produce frequences from the example data.

python test_em.py

Results can be found in output directory.

How to contribute:

1. Fork the repository: https://github.com/nmdp-bioinformatics/py-graph-em.git This will create a new repository with the given name e.g. py-graph-em.
2. Clone the repository locally

   git clone  https://github.com/pbashyal-nmdp/py-graph-em.git
   cd py-graph-em
   

3. Make a virtual environment and activate it, run make venv

    > make venv
      python3 -m venv venv --prompt py-graph-em-venv
      =====================================================================
    To activate the new virtual environment, execute the following from your shell
    source venv/bin/activate
   

4. Source the virtual environment

   source venv/bin/activate
   

5. Development workflow is driven through Makefile. Use make 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.
   

6. Install all the development dependencies. Will install packages from all requirements-*.txt files.

    make install
   

7. 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_my_project_template.py
   

8. Package Module files go in the my_project_template directory.

   my_project_template
   |-- __init__.py
   |-- algorithm
   |   `-- match.py
   |-- model
   |   |-- allele.py
   |   `-- slug.py
   `-- my_project_template.py
   

9. Run all tests with make test or different tests with make behave or make pytest. make behave will generate report files and open the browser to the report.
10. Use python app.py to run the Flask service app in debug mode. Service will be available at http://localhost:8080/
11. Use make docker-build to build a docker image using the current Dockerfile.
12. make docker will build and run the docker image with the service. Service will be available at http://localhost:8080/

The py-graph-em code was developed in the YOLO Lab and at the NMDP. Please cite Israeli, S., Gragert, L., Maiers, M., & Louzoun, Y. (2021). HLA haplotype frequency estimation for heterogeneous populations using a graph-based imputation algorithm. Human Immunology, 82(10), 746-757.‏

======= History

0.0.1 (2021-08-25)

• First release on PyPI.