https://github.com/limix/struct-lmm
Structured Linear Mixed Model is a method to test for loci that interact with multiple environments.
https://github.com/limix/struct-lmm
Last synced: 6 months ago
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Structured Linear Mixed Model is a method to test for loci that interact with multiple environments.
- Host: GitHub
- URL: https://github.com/limix/struct-lmm
- Owner: limix
- License: other
- Created: 2017-04-27T16:22:30.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-16T17:25:27.000Z (over 4 years ago)
- Last Synced: 2024-09-29T14:36:23.340Z (8 months ago)
- Language: Python
- Homepage:
- Size: 369 KB
- Stars: 14
- Watchers: 4
- Forks: 10
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Struct-LMM
Structured Linear Mixed Model (StructLMM) is a computationally efficient method to
test for and characterize loci that interact with multiple environments [1].
This a standalone module that implements the basic functionalities of StructLMM.
However, we recommend using StructLMM via
[LIMIX2](https://limix.readthedocs.io/en/2.0.x/index.html) as this additionally
implements:
- Multiple methods for GWAS;
- Methods to characterize GxE at specific variants;
- Command line interface.
## Install
From terminal, it can be installed using [pip](https://pypi.org/pypi/pip):
```bash
pip install struct-lmm
```
## Usage
```python
>>> from numpy import ones, concatenate
>>> from numpy.random import RandomState
>>>
>>> from struct_lmm import StructLMM
>>>
>>> random = RandomState(1)
>>> n = 20
>>> k = 4
>>> y = random.randn(n, 1)
>>> E = random.randn(n, k)
>>> M = ones((n, 1))
>>> x = 1.0 * (random.rand(n, 1) < 0.2)
>>>
>>> lmm = StructLMM(y, M, E)
>>> lmm.fit(verbose=False)
>>> # Association test
>>> pv = lmm.score_2dof_assoc(x)
>>> print(pv)
0.8470017313426488
>>> # Association test
>>> pv, rho = lmm.score_2dof_assoc(x, return_rho=True)
>>> print(pv)
0.8470017313426488
>>> print(rho)
0
>>> M = concatenate([M, x], axis=1)
>>> lmm = StructLMM(y, M, E)
>>> lmm.fit(verbose=False)
>>> # Interaction test
>>> pv = lmm.score_2dof_inter(x)
>>> print(pv)
0.6781100453132024
```
## Problems
If you encounter any problem, please, consider submitting a [new issue](https://github.com/limix/struct-lmm/issues/new).
## Authors
- [Danilo Horta](https://github.com/horta)
- [Francesco Paolo Casale](https://github.com/fpcasale)
- [Oliver Stegle](https://github.com/ostegle)
- [Rachel Moore](https://github.com/rm18)
## License
This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/struct-lmm/master/LICENSE.md).
[1] Moore, R., Casale, F. P., Bonder, M. J., Horta, D., Franke, L., Barroso, I., &
Stegle, O. (2018). [A linear mixed-model approach to study multivariate
gene–environment interactions](https://www.nature.com/articles/s41588-018-0271-0) (p. 1). Nature Publishing Group.