Skip to main content

No project description provided

Project description

Py-Graph-Imputation-MLO

Overview

py-graph-imputation-MLO is a Python package for graph-based imputation of missing data in genetic datasets. It leverages the py-graph-imputation library and provides additional functionality for filtering and processing imputed results.

Installation

To install the package, use the following command:

pip install py-graph-imputation-MLO

Dependencies

The package requires the following dependencies:

  • py-graph-imputation
  • cython == 0.29.32
  • numpy >= 1.20.2
  • pandas
  • tqdm

These dependencies will be installed automatically when you install the package.

Usage

Running the Imputation

To run the imputation process, use the RunGrim.py script. You can configure the process using a JSON configuration file. Here is an example of how to run the script:

python RunGrim.py

Configuration

The configuration file should be in JSON format and include the following fields:

  • freq_file: Path to the frequency file.
  • imputation_in_file: Path to the input file for imputation.
  • imputation_out_path: Path to the output directory for imputation results.
  • imputation_out_hap_freq_filename: Filename for the haplotype frequency output.
  • imputation_out_umug_freq_filename: Filename for the UMUG frequency output.
  • imputation_out_umug_pops_filename: Filename for the UMUG populations output.
  • imputation_out_hap_pops_filename: Filename for the haplotype populations output.
  • imputation_out_miss_filename: Filename for the missing data output.
  • output_MUUG: Boolean flag to enable/disable MUUG output.
  • output_haplotypes: Boolean flag to enable/disable haplotype output.
  • number_of_results: Number of results to output.
  • number_of_pop_results: Number of population results to output.

Example Configuration

Here is an example of a minimal configuration file:

{
  "freq_file": "path/to/frequency/file",
  "imputation_in_file": "path/to/input/file",
  "imputation_out_path": "path/to/output/directory",
  "imputation_out_hap_freq_filename": "hap_freq.csv",
  "imputation_out_umug_freq_filename": "umug_freq.csv",
  "imputation_out_umug_pops_filename": "umug_pops.csv",
  "imputation_out_hap_pops_filename": "hap_pops.csv",
  "imputation_out_miss_filename": "miss.csv",
  "output_MUUG": true,
  "output_haplotypes": true,
  "number_of_results": 10,
  "number_of_pop_results": 5
}

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or inquiries, please contact Regev Yehezkel Imra at regevel2006@gmail.com.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

py_graph_imputation_mlo-0.1.2.tar.gz (3.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

py_graph_imputation_MLO-0.1.2-py3-none-any.whl (3.6 kB view details)

Uploaded Python 3

File details

Details for the file py_graph_imputation_mlo-0.1.2.tar.gz.

File metadata

  • Download URL: py_graph_imputation_mlo-0.1.2.tar.gz
  • Upload date:
  • Size: 3.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.11.7

File hashes

Hashes for py_graph_imputation_mlo-0.1.2.tar.gz
Algorithm Hash digest
SHA256 9bbd4d3b249228de0bd9b00fd6ec2fe1b4abdd429a5a0b312189f1a48af0ea58
MD5 ae98eb2bb63e342ad3dfcc6dda4f3adb
BLAKE2b-256 0f305a04f2bb4f1ddc0f1661c3fb7d21ced35a427c7b3ce45b6609cb4401ca3b

See more details on using hashes here.

File details

Details for the file py_graph_imputation_MLO-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for py_graph_imputation_MLO-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 258e40ade4fca6454ac911874df60d012282e7af9099a9674578fd064289c9c1
MD5 91f786ee9e5c34ff7ab25bf18bb30795
BLAKE2b-256 e61ffa9e4c1d044f557eed71b12acea9320baa0202dd4e8aadc4e041af3868ed

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page