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PolyLinkR

Overview

PolyLinkR is an R package that performs gene-based pathway enrichment, which can also be used as evidence for polygenic selection in case the software is used with selection signals evidence. The package explicitly also accounts for linkage disequilibrium between adjacent loci belonging on the same pathway.

PolyLinkR introduces several improvements and faster implementations of the popular polygenic selection tool PolySel, which builds upon the core file types and summary statistics used in PolySel. The key difference between PolyLinkR and PolySel is the algorithm used to generate the null distribution of pathway scores. PolySel performs a standard permutation to remap gene scores to genes, whereas PolyLinkR uses a permutation algorithm that randomly links all chromosomes/contigs into a single ‘circular’ genome, and then rotates this circular genome to create a unique mapping between the genes and gene scores. Importantly, this randomisation process preserves the innate linkage structure amongst the genes across the genome, limiting the number of potential false positives that might arise otherwise.

PolyLinkR uses a four-step workflow:

  1. Read data with plR_read()
  2. Generate permutation nulls with plR_permute()
  3. Rescale for autocorrelation with plR_rescale()
  4. Prune and identify significant sets with plR_prune()

Installation

Install from CRAN:

install.packages("polylinkR")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("ACAD-UofA/PolyLinkR")

Usage

For example:

library(polylinkR)

# Step 1: Read your data
plr_obj <- plR_read(input.path = "path/to/your/data")

# Step 2: Generate permutation nulls
plr_perm <- plR_permute(plr_obj, n.perm = 1e5)

# Step 3: Rescale for genetic autocorrelation
plr_rescale <- plR_rescale(plr_perm)

# Step 4: Prune and identify significant gene sets
plr_final <- plR_prune(plr_rescale)

Documentation

Visit our pkgdown site for comprehensive documentation including:

  • Getting Started - Basic workflow and quick start guide
  • Articles - In-depth tutorials on:
    • Controlling for confounders and covariates
    • Using recombination rates
    • Parallel processing for large datasets
    • Input formats and parameters reference
  • Function Reference - Complete API documentation

Getting Help

If you encounter a bug or have a feature request, please open an issue on GitHub. For general questions and discussions, please use GitHub Discussions.

Citation

If you use polylinkR in your research, please cite:

Souilmi et al. (2024). polylinkR: An R package for gene-based pathway enrichment
accounting for linkage disequilibrium. R package version 0.5.0.

For the latest citation information, use:

citation("polylinkR")