Name: GWAS_benchmark
Owner: Microsoft Genomics
Description: A set of tools for benchmarking or evaluating GWAS algorithms. A detailed description can be found in C. Widmer et al., Scientific Reports 2014.
Created: 2014-11-11 21:06:47.0
Updated: 2016-08-08 16:25:48.0
Pushed: 2015-09-04 18:25:07.0
Homepage: http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/
Size: 32115
Language: Python
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This python code can be used to benchmark or evaluate GWAS algorithms.
If you use this code, please cite:
See this website for related software:
http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/
Our documentation (including live examples) is available as ipython notebook: https://github.com/MicrosoftGenomics/GWAS_benchmark/blob/master/GWAS_benchmark/simulation.ipynb
(To start ipython notebook locally, type ipython notebook at the command line.)
This code contains the following modules:
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
cluster_data: module to compute and visualize a hierarchical clustering of GWAS data to get an understanding of its structure (population structure, family structure)
split_data_helper: helper module for splitting SNPs by chromosome
For testing purposes a small data set is provided at data/mouse (see the README file within that directory for the data license).
An 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.
We 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.
Further, we use the ipython-notebook to demonstrate some of the functionality of the hierarchical clustering module: http://nbviewer.ipython.org/github/MicrosoftGenomics/GWAS_benchmark/blob/master/GWAS_benchmark/simulation.ipynb
If you have pip installed, installation is as easy as:
install GWAS_benchmark
fastlmm has the following dependencies:
python 2.7
Packages:
We highly recommend using a python distribution such as Anaconda (https://store.continuum.io/cshop/anaconda/) or Enthought (https://www.enthought.com/products/epd/free/). Both these distributions can be used on linux and Windows, are free for non-commercial use, and optionally include an MKL-compiled distribution for optimal speed. This is the easiest way to get all the required package dependencies.
Go to the directory where you copied the source code for fastlmm.
On linux:
At the shell, type:
python setup.py install
On Windows:
At the OS command prompt, type
on setup.py install
When working on the developer version, just set your PYTHONPATH to point to the directory above the one named GWAS_benchmark in the source code. For e.g. if GWAS_benchmark is in the [somedir] directory, then in the unix shell use:
rt PYTHONPATH=$PYTHONPATH:[somedir]
Or in the Windows DOS terminal, one can use:
PYTHONPATH=%PYTHONPATH%;[somedir]
(or use the Windows GUI for env variables).
From the directory tests at the top level, run:
on 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 set your PYTHONPATH as described above to run the regression tests, and not “python setup.py install”.