https://github.com/foggalong/robustocs

Robust optimal contirbution selection problems for genetics with Python
https://github.com/foggalong/robustocs

breeding genomic-selection genomics python python-module selective-breeding

Last synced: 2 months ago
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Robust optimal contirbution selection problems for genetics with Python

• Host: GitHub
• URL: https://github.com/foggalong/robustocs
• Owner: Foggalong
• License: mit
• Created: 2024-02-20T13:34:30.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-08-09T14:04:00.000Z (5 months ago)
• Last Synced: 2024-08-09T16:23:35.951Z (4 months ago)
• Topics: breeding, genomic-selection, genomics, python, python-module, selective-breeding
• Language: Python
• Homepage: https://pypi.org/project/robustocs
• Size: 9.98 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Security: .github/SECURITY.md

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README

        
# RobustOCS

[![PyPi](https://img.shields.io/pypi/v/robustocs.svg)](https://pypi.python.org/pypi/robustocs) [![Check Build](https://github.com/Foggalong/RobustOCS/actions/workflows/check-build.yml/badge.svg?branch=main)](https://github.com/Foggalong/RobustOCS/actions/workflows/check-build.yml)

Tools for solving robust optimal contribution selection problems in Python. All code and examples in RobustOCS are fully and freely available for all use under the MIT License.

## Installation

The latest release can be installed from the [PyPI repository](https://pypi.org/project/robustocs/) using

```bash

pip install robustocs

```

Alternatively, it use the latest (potentially unstable) version from GitHub, use

```bash

git clone https://github.com/Foggalong/RobustOCS.git

pip install RobustOCS/

```

Either way, the package depends on Python 3.10+, using [NumPy](https://pypi.org/project/numpy) for linear algebra and [SciPy](https://scipy.org) for sparse matrix objects. As a solver it can either use [Gurobi](https://www.gurobi.com) (commercial) via [gurobipy](https://pypi.org/project/gurobipy) or [HiGHS](https://highs.dev) (free software) via [highspy](https://pypi.org/project/highspy).

## Quick-start

Suppose we have a breeding cohort whose relationships are modelled some matrix $\Sigma$, and whose expected breeding values have mean $\bar{\boldsymbol\mu}$ and variance $\Omega$. If these are [saved in files](https://github.com/Foggalong/RobustOCS/wiki/File-Formats), finding the robust optimal contribution selection is as simple as opening Python and running

```python

import robustocs as rocs

selection, objective, merit, coancestry = rocs.solveROCS(

    sigma_filename="cohort-relationships.txt",

    mu_filename="breeding-means.txt",

    omega_filename="breeding-variances.txt",

    sex_filename="cohort-sexes.txt",

    method='robust', lam=0.5, kappa=1

)

```

where `selection` will be an array of optimal selections, `objective` is a score of the selection, `merit` the expected genetic merit, and `coancestry` the group co-ancestry.

## Documentation

The [GitHub wiki] includes documentation which explains the usage and parameters in more detail, full rundowns of the key functions (including the quick-start's [`solveROCS`]), all alongside some worked examples using the more granular solver functions (the data for which is in [`examples/`](examples/)). This includes a realistic simulated example from [Gregor Gorjanc] and [Ivan Pocrnić].

[`solveROCS`]: https://github.com/Foggalong/RobustOCS/wiki/solveROCS

[GitHub wiki]: https://github.com/Foggalong/RobustOCS/wiki

[Gregor Gorjanc]: https://www.ed.ac.uk/profile/gregor-gorjanc

[Ivan Pocrnić]: https://www.ed.ac.uk/profile/ivan-pocrnic