Julia package for sum of single effects regression with generalized linear mixed model
Julia can be downloaded from Julia and is automatically recognized by JupyterLab if it has already been installed. Julia can also be called by simlink.
# Install Julia v1.7.2 (Feb 6, 2022)
wget https://julialang-s3.julialang.org/bin/linux/x64/1.7/julia-1.7.2-linux-x86_64.tar.gz
tar -xvf julia-1.7.2-linux-x86_64.tar.gz
# Export Julia into PATH or you can add this line into your ~/.bashrc file
export export PATH="./julia-1.7.2/bin:$PATH"Then by typing julia in the command line, Julia can be invoked
_
_ _ _(_)_ | Documentation: https://docs.julialang.org
(_) | (_) (_) |
_ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 1.7.2 (2022-02-06)
_/ |\__'_|_|_|\__'_| | Official https://julialang.org/ release
|__/ |
julia>You first need to add a package manager Pkg and add any necessary packages via Pkg
using Pkg
Pkg.add(url="https://github.com/cumc/SuSiEGLMM.jl.git") # SuSiEGLMM package
# Other necessary packages
Pkg.add(["Statistics", "Distributions", "StatsBase", "Random", "LinearAlgebra", "DelimitedFiles", "Distributed", "GLM"])
Pkg.add("Plots")Parallelization (@distributed) is performed at the chromosome level; that is, a set of SNPs in each chromosome is assigned to each worker (or process). One can generate workers up to the number of chromosomes. After that packages need to be loaded with @everyhwere, so that all workers can access the packages. Note that this distributed computing does not have to send all data to all workers; data are accessible on the main process only.
using Distributed
addprocs(2) # for sall data set case
using Statistics, Distributions, StatsBase, Random, LinearAlgebra
@everywhere using SuSiEGLMMFine mapping for SuSiE-GLM is run by fineQTL_glm. For help, type ?fineQTL_glm about the details.