Advanced Source Installation Methods

Overview

These installation methods are for advanced users and developers who need to install SAIGEQTL without using Pixi. They require manual system configuration and dependency management.

⚠️ Note: These methods require you to configure your system with the necessary compilers and libraries. If you prefer an easier approach, we strongly recommend the Pixi Source Installation which handles all dependencies automatically.

Why Use These Methods?

✅ Advantages:

• Customizable builds and configurations
• Integration with existing R environment

⚠️ Disadvantages:

• Requires C++ compiler setup on your system
• Can fail due to missing system libraries
• No pre-built package
• Dependency version conflicts possible
• Slower installation (compilation time)
• Complex troubleshooting on older systems

System Requirements

• R ≥ 3.5.0 (≥ 4.0 recommended)
• C/C++ compiler (gcc/clang with C++11 support)
• BLAS/LAPACK libraries (automatically detected by R)
• OpenMP (optional, for parallel processing)
• Git (for downloading source code)
• GNU make/cmake

System Libraries (should be installed before R package requirements)

• zlib & zstd (Compression & Zstandard compression)
• SuperLu (Sparse linear solver)
• Boost C++ libraries (String algorithms)

Dependencies

These dependencies should be installed in following guide (good to check if they exist if running in to installation issues)

R Package Dependencies

• Rcpp (≥ 1.0.7)
• RcppArmadillo (≥ 0.10.7.5)
• RcppParallel, RcppEigen, RcppNumerical, Matrix, data.table, furrr, dbplyr, BH

  Additional LinkingTo Dependencies

• SPAtest (3.1.2)
• SKAT, RhpcBLASctl, RSQLite, dplyr, nlme, MASS, optparse
• packages that can be installed from GitHub: cysouw/qlcMatrix, leeshawn/MetaSKAT, barkasn/fastSave

Platform-Specific Compiler Setup

Linux (Ubuntu/Debian)

sudo apt update && sudo apt install build-essential r-base-dev

Linux (CentOS/RHEL)

sudo yum groupinstall "Development Tools"
sudo yum install R-devel

macOS

# Install Xcode command line tools
xcode-select --install

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Installation Methods

Method 1: R remotes

# Install using remotes package
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}
remotes::install_github("weizhou0/SAIGEQTL", dependencies = TRUE)

# Test installation
library(SAIGEQTL)
packageVersion("SAIGEQTL")

Method 2: Manual Git Clone + Install

# 1. Download source code
git clone https://github.com/weizhou0/SAIGEQTL.git
cd SAIGEQTL

# 2. Install dependencies (if needed)
Rscript -e 'install.packages(c("Rcpp", "RcppArmadillo", "Matrix", "data.table", "optparse"))'

# 3. Install SAIGEQTL package
R CMD INSTALL .

# 4. Test installation
R -e 'library(SAIGEQTL); packageVersion("SAIGEQTL")'

Method 3: Custom Library Path

# Install to specific directory
mkdir -p ~/R-packages
R CMD INSTALL --library=~/R-packages SAIGEQTL/

# Test with custom library
R -e 'library(SAIGEQTL, lib.loc="~/R-packages"); packageVersion("SAIGEQTL")'

Method 4: Development Installation

# For contributors and developers
git clone https://github.com/weizhou0/SAIGEQTL.git
cd SAIGEQTL
Rscript scripts/install.R --dev

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Usage

Standard Source Installation

library(SAIGEQTL)

Custom Library Usage

🆕 Recommended: Use the --library parameter with wrapper scripts:

step1_fitNULLGLMM_qtl.R --library=/path/to/custom/library [other_options]

Alternative: Direct R usage with lib.loc:

library(SAIGEQTL, lib.loc="/path/to/custom/library")

Command-Line Scripts

After installation, executable scripts are available in:

/path/to/SAIGEQTL/extdata/step1_fitNULLGLMM_qtl.R
/path/to/SAIGEQTL/extdata/step2_tests_qtl.R
/path/to/SAIGEQTL/extdata/step3_gene_pvalue_qtl.R
/path/to/SAIGEQTL/extdata/makeGroupFile.R

💡 Tip: Add to your PATH for convenience:

# Add to ~/.bashrc or ~/.bash_profile
export PATH="/path/to/SAIGEQTL/extdata:$PATH"

# Then you can run:
step1_fitNULLGLMM_qtl.R --help

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✅ Next Steps

Installation Complete!

You’ve successfully built SAIGEQTL from source. Here’s how to get started with your first analysis.

Your Command Prefix

# Scripts located in: /path/to/SAIGEQTL/extdata/
Rscript /path/to/SAIGEQTL/extdata/step1_fitNULLGLMM_qtl.R [options]

Quick Start Tutorial

Ready to run your first analysis? Follow this tutorial:

📖 cis-eQTL Analysis Tutorial - Learn how to map local genetic effects on gene expression

What you’ll learn:

• How to prepare your data files
• Running Step 1: Fit the null model
• Running Step 2: Test genetic variants
• Running Step 3: Calculate gene-level p-values

Additional Resources

• Parameters & Options - Complete reference for all command-line parameters
• Running Scripts Guide - Detailed information on running SAIGE-QTL scripts
• FAQ - Common questions and troubleshooting

Need Help?

If you encounter issues:

1. Check the FAQ for common problems
2. Verify R can find the SAIGEQTL library
3. Review the cis-eQTL tutorial for step-by-step guidance