MVtest is an analysis tool that can be run on many common file formats using syntax similar to programs you've probably already used. Instructions for installation can be found below.
MVtest requires python 3.7.x as well as the following libraries:
- NumPy (version 1.16.2 or later) www.numpy.org
- SciPY (version 1.3.0 or later) www.scipy.org
- pytabix (version 0.1 or later) https://pypi.org/project/pytabix/
- bgen-reader (version 3.0.6 or later) https://pypi.org/project/bgen-reader/
- libGWAS (version 1.2.0 or later) https://github.com/edwards-lab/libGWAS
MVtest's installation will attempt to install these required components for you, however, it requires that you have write permission to the installation directory. If you are using a shared system and lack the necessary privileges to install libraries and software yourself, you should please see one of the sections, Miniconda or virtual-env below for instructions on different options for setting up your own python environement which will exist entirely under your own control.
To install MVTest, simply clone the sources using the following command:
$ git clone https://github.com/edwards-lab/MVtest
Or you may visit the website and download the tarball directly from github: https://github.com/edwards-lab/MVtest
Once you have downloaded the software, simply extract the contents and run the following command to install it:
$ pip install .
If no errors are reported, it should be installed and ready to use.
Regarding PYTHON 2 I have completely switched over to python 3 without trying to remain compatible with Python 2 because the bgen_reader no longer supports 2 and the end of life is only a few months from my writing this.
As such, if you wish to use python2, you will need to install an older version.
Aside from the library dependencies, MVtest's requirements depend largely on the number of SNPs and individuals being analyzed as well as the data format being used. In general, GWAS sized datasets will require several gigabytes of memory when using the traditional pedigree format, however, even 10s of thousands of subjects can be analyzed with less than 1 gigabyte of RAM when the data is formatted as transposed pedigree or PLINK's default bed format.
Otherwise, it is recommended that the system be run on a unix-like system such as Linux or OS X, but it should work under windows as well (we can't offer support for running MVtest under windows).
MVtest comes with a unit test suite which can be run prior to installation. To run the tests, simply run the following command from within the root directory of the extracted archive's contents:
$ pytest
If no errors are reported, then mvtest should run correctly on your system.
Virtual ENV is a powerful too for python programmers and end users alike as it allows for users to deploy different versions of python applications without the need for root access to the machine.
Because MVtest requires version 3.7, you'll need to ensure that your machine's python version is in compliance. Virtual Env basically uses the the system version of python, but creates a user owned environment wrapper allowing users to install libraries easily without administrative rights to the machine.
For a helpful introduction to VirtualEnv, please have a look at the tutorial: http://www.simononsoftware.com/virtualenv-tutorial/
Miniconda is a minimal version of the package manager used by the Anaconda python distribution. It makes it easy to create local installations of python with the latest versions of the common scientific libraries for users who don't have root access to their target machines. Basically, when you use miniconda, you'll be installing your own version of Python into a directory under your control which allows you to install anything else you need without having to submit a helpdesk ticket for administrative assistance.
Unlike pip, the folks behind the conda distributions provide binary downloads of it's selected library components. As such, only the most popular libraries, such as pip, NumPY and SciPy, are supported by conda itself. However, these do not require compilation and may be easier to get installed than when using pip alone. I have experienced difficulty installing SciPy through pip and setup tools on our cluster here at vanderbilt due to non-standard paths for certain required components, but mini-conda always comes through.
Firstly, download and install the appropriate version of miniconda at the project website. Please be sure to choose the Python 2 version: http://conda.pydata.org/miniconda.html
While it is doing the installation, please allow it to update your PATH information. If you prefer not to always use this version of python in the future, simple tell it not to update your .bashrc file and note the instructions for loading and unloading your new python environment. Please note that even if you chose to update your .bashrc file, you will need to follow directions for loading the changes into your current shell.
Once those changes have taken effect, install setuptools and scipy: $ conda install pip scipy
Installing SciPy will also force the installation of NumPy, which is also required for running mvtest. (setuptools includes easy_install).
Once that has been completed successfully, you should be ready to follow the standard instructions for installing mvtest.
The online manual can be found at http://edwards-lab.github.io/MVtest/
For developers who would like to use the GWAS parsers, the API manual can be found at http://edwards-lab.github.io/MVtest/api/index.html