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Fast GWAS

Project 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.

Improvements:

A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.

Documentation

Code

Contacts

Quick install:

If you have pip installed, installation is as easy as:

pip install fastlmm

Detailed Package Install Instructions:

fastlmm has the following dependencies:

python 3.7+ or python 2.7+

Packages:

  • scipy
  • numpy
  • pandas
  • matplotlib
  • scikit.learn (sklearn)
  • dill
  • mkl
  • pysnptools
  • cython
  • optional: [statsmodels -- install only required for logistic-based tests, not the standard linear LRT]

(1) Installation of dependent packages

We recommend using a Python distribution such as Anaconda. This distribution can be used on Linux and Windows and is free. It is the easiest way to get all the required package dependencies.

(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

Note, if this fails with a gcc permission denied error, then specifying the correct compiler will likely fix the problem, e.g.

python setup.py build_ext --inplace --compiler=msvc

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. If you have VisualStudio2008 installed (which was used to build python2.7) you need to nothing more. Otherwise, follow these instructions:

If you have Visual Studio 2010 installed, execute:

SET VS90COMNTOOLS=%VS100COMNTOOLS%

or with Visual Studio 2012 installed:

SET VS90COMNTOOLS=%VS110COMNTOOLS%

or with Visual Studio 2013 installed:

SET VS90COMNTOOLS=%VS120COMNTOOLS%

or with Visual Studio 2015 installed:

SET VS90COMNTOOLS=%VS130COMNTOOLS%

or with Visual Studio 2017 installed:

SET VS90COMNTOOLS=%VS140COMNTOOLS%

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".

Project details


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Files for fastlmm, version 0.4.2
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Filename, size fastlmm-0.4.2-cp37-cp37m-win_amd64.whl (11.9 MB) File type Wheel Python version cp37 Upload date Hashes View hashes
Filename, size fastlmm-0.4.2.tar.gz (59.7 MB) File type Source Python version None Upload date Hashes View hashes

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