Metadata-Version: 2.1
Name: struct-lmm
Version: 0.3.2
Summary: Linear mixed model to study multivariate genotype-environment interactions
Home-page: https://github.com/limix/struct-lmm
Author: D. Horta, P. Casale, R. Moore
Author-email: rm18@sanger.ac.uk
Maintainer: Danilo Horta
Maintainer-email: horta@ebi.ac.uk
License: MIT
Download-URL: https://github.com/limix/struct-lmm
Description: # 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.
        
Keywords: lmm,gwas,environment
Platform: Windows
Platform: MacOS
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
