Installation

Follow the following steps to set up the environment for running HaploC tools

1. Clone the repository: git clone https://github.com/CSOgroup/HaploC-tools.git
2. Download data files required by HaploC-tools (1000 Genomes reference panel haplotypes, genomic reference data) and put (or link) it under HaploC-tools. The data files can be downloaded from zenodo.

# Within the HaploC-tools folder run:

wget --content-disposition https://zenodo.org/records/10446020/files/genomicData.tar.gz?download=1
tar -xvzf genomicData.tar.gz

You can also get a demo data folder to test HaploC-tools with:

wget --content-disposition https://zenodo.org/records/10446020/files/demo_data.tar.gz?download=1
tar -xvzf demo_data.tar.gz

This can be saved outside the HaploC-tools folder and be used in the following steps to run an end-to-end analysis.

3. Create conda environment with specified dependencies

conda env create -f HaploC-tools/environments/env_HapCUT2.yml # environment required for HapCUT2-based operations
conda env create -f HaploC-tools/environments/env_nHapCUT2.yml # environment required for all other operations

To fasten up the procedure, you can consider using mamba, a drop-in replacement for conda.

4. Install required R packages under the nHapCUT2 environment:

• R.utils (>= 2.9.0),
• doParallel (>= 1.0.15),
• ape (>= 5.3),
• dendextend (>= 1.12.0),
• fitdistrplus (>= 1.0.14),
• igraph (>= 1.2.4.1),
• Matrix (>= 1.2.17),
• rARPACK (>= 0.11.0),
• factoextra (>= 1.0.5),
• data.table (>= 1.12.2),
• fields (>= 9.8.3),
• GenomicRanges (>= 1.36.0)
• ggplot2 (>= 3.3.5)
• strawr (>= 0.0.9)

Also install CALDER R packages under the nHapCUT2 environment:

install.packages("HaploC-tools/CALDER2/", repos = NULL, type = "source")

Packages can be installed and checked with the following steps:

First, activate the nHapCUT2 env.

conda activate nHapCUT2

Next, execute the following:

install_if_needed <- function(package, version = NULL) {
    if (!require(package, character.only = TRUE)) {
        if (!is.null(version)) {
            package <- paste0(package, "_", version)
        }
        install.packages(package, dependencies = TRUE)
    }
}

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

cran_packages <-
    c(
        "R.utils",
        "doParallel",
        "ape",
        "dendextend",
        "fitdistrplus",
        "Matrix",
        "rARPACK",
        "factoextra",
        "data.table",
        "fields",
        "ggplot2",
        "strawr"
    )

## Install igraph with conda as it requires specific libraries
system("conda install conda-forge::r-igraph")

bioconductor_packages <- c("GenomicRanges")

for (pkg in cran_packages) {
    install_if_needed(pkg)
}

for (pkg in bioconductor_packages) {
    BiocManager::install(pkg)
}

# Install calder
install.packages("./HaploC-tools/CALDER2/", repos = NULL, type = "source")

## Check

pkgs <- c(cran_packages, bioconductor_packages, "CALDER")

check_package <- function(package) {
    if (!require(package, character.only = TRUE)) {
        cat(sprintf("Package '%s' is not installed.\n", package))
        return(FALSE)
    } else {
        cat(sprintf("Package '%s' is installed. ", package))
        success <- require(package, character.only = TRUE)
        if (success) {
            cat("Loading successful.\n")
            return(TRUE)
        } else {
            cat("Loading failed.\n")
            return(FALSE)
        }
    }
}

# Check each package
results <- sapply(pkgs, check_package)

# Print summary
cat("\nSummary of package checks:\n")
print(results)

if (all(results)) {
    print("All packages correctly installed!")
} else {
    print("Some packages can't be loaded or installed, please check.")
}

Please ensure you are in the directory containing HaploC-tools to install CALDER based on its path successfully. If all packages are installed and working properly, you should get the "All packages correctly installed!" message, and you can proceed.

Next steps

• Prepare working directory
• Run HaploC-tools with slurm
• Run HaploC-tools without slurm
• Downstream-analysis