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        "description": "## GWAS_benchmark\n-------------------------------------\n\nThis python code can be used to benchmark or evaluate GWAS algorithms.\n  \nIf you use this code, please cite:\n\n* C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C. Kadie, R. Davidson, J. Listgarten, and D. Heckerman, Further Improvements to Linear Mixed Models for Genome-Wide Association Studies, _Scientific Reports_ **4**, 6874, Nov 2014 (doi:10.1038/srep06874).\n\nSee this website for related software:  \nhttp://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/\n\nOur documentation (including live examples) is available as ipython notebook:\nhttps://github.com/MicrosoftGenomics/GWAS_benchmark/blob/master/GWAS_benchmark/simulation.ipynb\n\n(To start ipython notebook locally, type `ipython notebook` at the command line.)\n\nThis code contains the following modules:\n\n* semisynth_experiments: the core module for generating synthetic phenotypes based on real snps, running different methods for GWAS and evaluating them all within one pipeline\n\n* cluster_data: module to compute and visualize a hierarchical clustering of GWAS data to get an understanding of its structure (population structure, family structure)\n\n* split_data_helper: helper module for splitting SNPs by chromosome\n\nFor testing purposes a small data set is provided at `data/mouse` (see the `README` file within that directory for the data license).\n\nAn example run to compute type I error rate on the mouse data using 10 causal SNPs can be executed by running `python run_simulation.py`.\n\nWe recommend running this example on a cluster computer as this simulation is computationally demanding. An example result plot (of type I error) is provided in the results directory.\n\nFurther, we use the ipython-notebook to demonstrate some of the functionality of the hierarchical clustering module: \nhttp://nbviewer.ipython.org/github/MicrosoftGenomics/GWAS_benchmark/blob/master/GWAS_benchmark/simulation.ipynb\n\n### Quick install:\n\n\nIf you have pip installed, installation is as easy as:\n\n```\npip install GWAS_benchmark\n```\n\n\n### Detailed Package Install Instructions:\n\n\nfastlmm has the following dependencies:\n\npython 2.7\n\nPackages:\n\n* numpy\n* scipy\n* matplotlib\n* pandas\n* scikit.learn (sklearn)\n* fastcluster\n* fastlmm\n* pysnptools\n* optional: [statsmodels -- install only required for logistic-based tests, not the standard linear LRT]\n\n\n#### (1) Installation of dependent packages\n\nWe highly recommend using a python distribution such as \nAnaconda (https://store.continuum.io/cshop/anaconda/) \nor Enthought (https://www.enthought.com/products/epd/free/).\nBoth these distributions can be used on linux and Windows, are free \nfor non-commercial use, and optionally include an MKL-compiled distribution\nfor optimal speed. This is the easiest way to get all the required package\ndependencies.\n\n\n#### (2) Installing from source\n\nGo to the directory where you copied the source code for fastlmm.\n\nOn linux:\n\nAt the shell, type: \n```\nsudo python setup.py install\n```\n\nOn Windows:\n\nAt the OS command prompt, type \n```\npython setup.py install\n```\n\n\n### For developers (and also to run regression tests)\n\nWhen working on the developer version, just set your PYTHONPATH to point to the directory\nabove the one named GWAS_benchmark in the source code. For e.g. if GWAS_benchmark is \nin the [somedir] directory, then in the unix shell use:\n```\nexport PYTHONPATH=$PYTHONPATH:[somedir]\n```\nOr in the Windows DOS terminal, one can use: \n```\nset PYTHONPATH=%PYTHONPATH%;[somedir]\n```\n(or use the Windows GUI for env variables).\n\n#### Running regression tests\n\nFrom the directory tests at the top level, run:\n```\npython test.py\n```\nThis will run a\nseries of regression tests, reporting \".\" for each one that passes, \"F\" for each\none that does not match up, and \"E\" for any which produce a run-time error. After\nthey have all run, you should see the string \"............\" indicating that they \nall passed, or if they did not, something such as \"....F...E......\", after which\nyou can see the specific errors.\n\nNote that you must set your PYTHONPATH as described above to run the \nregression tests, and not \"python setup.py install\".",
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