Metadata-Version: 2.1
Name: fastlmm
Version: 0.4.12
Summary: Fast GWAS
Home-page: https://fastlmm.github.io/
Author: FaST-LMM Team
Author-email: fastlmm-dev@python.org
License: Apache 2.0
Project-URL: Bug Tracker, https://github.com/fastlmm/FaST-LMM/issues
Project-URL: Documentation, http://fastlmm.github.io/FaST-LMM
Project-URL: Source Code, https://github.com/fastlmm/FaST-LMM
Description: FaST-LMM
        =================================
        
        FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing 
        genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples.
        
        This release contains the following features, each illustrated with an IPython notebook.
        
        * Core FaST-LMM ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html)
        
        Improvements:
        
        * Ludicrous-Speed GWAS ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)) -- [Kadie and Heckerman, *bioRxiv* 2018](https://www.biorxiv.org/content/10.1101/154682v2)
        * Heritability with Spatial Correction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)), [Heckerman *et al.*, *PNAS* 2016](http://www.pnas.org/content/113/27/7377.abstract)
        * Two Kernels ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Widmer *et al.*, *Scientific Reports* 2014](http://www.nature.com/srep/2014/141112/srep06874/full/srep06874.html)
        * Set Analysis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Bioinformatics* 2014](http://bioinformatics.oxfordjournals.org/content/early/2014/09/07/bioinformatics.btu504)
        * Epistasis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Scientific Reports,* 2013](http://www.nature.com/srep/2013/130122/srep01099/full/srep01099.html)
        * Prediction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html)
        
        *A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.*
        
        
        Documentation
        =================================
        
        * IPython Notebooks:
        	* [Core, Epistasis, Set Analysis, Two Kernels](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)
        	* [Heritability with Spatial Correction](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)
        	* [Ludicrous-Speed GWAS](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)
        * [API Documentation](http://fastlmm.github.io/FaST-LMM/)
        * [Project Home and Full Annotated Bibliography](https://fastlmm.github.io/)
        
        
        Code
        =================================
        * [PyPi](https://pypi.org/project/fastlmm/)
        * [GitHub](https://github.com/fastlmm/FaST-LMM)
        
        Contacts
        =================================
        
        * Email the developers at fastlmm-dev@python.org.
        * [Join](mailto:fastlmm-user-join@python.org?subject=Subscribe) the user discussion and announcement list (or use [web sign up](https://mail.python.org/mailman3/lists/fastlmm-user.python.org)).
        * [Open an issue](https://github.com/fastlmm/FaST-LMM/issues) on GitHub.
        
        Quick install:
        =================================
        
        If you have Anaconda installed, installation is as easy as:
        
            conda install "mkl>=2019.4" "scipy" "numpy"
            pip install fastlmm
        
        (1) Installation of dependent packages
        -------------------------------------------
        
        You must have the "mkl" (and related) packages installed. It is not available via pip,
        but the conda command above will install it.
        
        We recommend using a Python distribution such as 
        [Anaconda](https://www.anaconda.com/distribution/).
        This distribution can be used on Linux, Windows, and Mac and is free.
        It is the easiest way to get all the required package
        dependencies, especially the those related to the
        MKL library.
        
        
        (2) Installing from source
        -------------------------------------------
        
        Go to the directory where you copied the source code for fastlmm.
        
        On Linux:
        
        At the shell, type: 
        
            sudo python setup.py install
        
        
        On Windows:
        
        At the OS command prompt, type 
        
            python setup.py install
        
        
        
        For developers (and also to run regression tests)
        =====================================================
        
        When working on the developer version, first add the src directory of the package to your PYTHONPATH 
        environment variable.
        
        For building C-extensions, first make sure all of the above dependencies are installed (including cython)
        
        To build extension (from .\src dir), type the following at the OS prompt:
        
            python setup.py build_ext --inplace
        
        
        Don't forget to set your PYTHONPATH to point to the directory above the one named fastlmm in
        the fastlmm source code. For e.g. if fastlmm is in the [somedir] directory, then
        in the unix shell use:
        
            export PYTHONPATH=$PYTHONPATH:[somedir]
        
        Or in the Windows DOS terminal,
        one can use: 
        
            set PYTHONPATH=%PYTHONPATH%;[somedir]
        
        (or use the Windows GUI for env variables).
        
        Note for Windows: You must have Visual Studio installed.
        
        Running regression tests
        --------------------------------------
        
        From the directory tests at the top level, run:
        
            python test.py
        
        This will run a
        series of regression tests, reporting "." for each one that passes, "F" for each
        one that does not match up, and "E" for any which produce a run-time error. After
        they have all run, you should see the string "............" indicating that they 
        all passed, or if they did not, something such as "....F...E......", after which
        you can see the specific errors.
        
        Note that you must use "python setup.py build_ext --inplace" to run the 
        regression tests, and not "python setup.py install".
        
        
Keywords: gwas bioinformatics LMMs MLMs linear mixed models genomics genetics python
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
