Installation - single-cell-genetics/limix_qtl Wiki

Main software installation

The LIMIX QTL pipeline can be downloaded directly from github and placed where you want to use it.

Direct download link: git clone https://github.com/single-cell-genetics/limix_qtl.git

Next to the github package, you need install its dependencies. We recommend you either use conda or docker / singularity for this. Instructions below.

Dependencies

Installation using conda

We recommend you install the limix based QTL mapping pipeline in a separate conda environment. Here we create the environment "limix_qtl", and use limix version 2. For faster installations consider using mamba instead of conda, (by replacing conda with mamba).

conda create -n limix_qtl python=3.7 anaconda

source activate limix_qtl

conda install -c anaconda pytest pytables

conda install -c conda-forge bgen=4 pandas-plink

pip install bgen-reader

pip install glimix-core

Installation using docker/singularity

Bootstrap: docker
From: continuumio/miniconda3
Bootstrap: docker
From: continuumio/miniconda3
######################## Limix based QTL mapping ###########################
%runscript
    export PATH=/opt/conda/bin:${PATH}
    /bin/bash
%post
    export PATH=/opt/conda/bin:${PATH}
    git clone https://github.com/single-cell-genetics/limix_qtl.git
    # Initiate conda
    conda package
    conda config --add channels conda-forge
    # Install C & Python packages with conda.
    conda install -c conda-forge pytest pytables matplotlib-venn bgen=4 pandas-plink
    # Install remaining Python packages with pip
    pip install bgen-reader
    pip install glimix-core
    # Install R packages (from within R)
    R --slave -e 'install.packages(c("BiocManager","dplyr","readr"))'
    R --slave -e 'BiocManager::install(c("qvalue","multtest","rhdf5"))'
##############################################################################


Installation notes

Some filesystems have file locking disabled, to be able to use the tool, which by default tries to lock the output file, use: export HDF5_USE_FILE_LOCKING=FALSE

Currently we only support limix 2, so please check you have installed the correct version.

Please check and make sure your numpy is bound to intel MKL, which makes the analyses much faster (especially on intel machines). Please find further information here