Overview
polylinkR is an R package for gene-based pathway
enrichment that accounts for linkage disequilibrium among loci. This
vignette demonstrates the basic workflow using the included
tiny_polylinkR example dataset.
The Four-Step Workflow
The polylinkR workflow follows four sequential steps:
- plR_read() - Read and validate input files
- plR_permute() - Generate permutation nulls and compute p-values
- plR_rescale() - Rescale for genetic autocorrelation
- plR_prune() - Prune gene sets and compute FDR
Step 1: Read Input Data
First, we read the example data included with the package:
# Locate the example data
data_path <- system.file("extdata", "tiny_polylinkR", package = "polylinkR")
data_path
#> [1] "/home/runner/work/_temp/Library/polylinkR/extdata/tiny_polylinkR"
# Read the data
plr_obj <- plR_read(
input.path = data_path,
verbose = FALSE
)
# Check the result
class(plr_obj)
#> [1] "plR"
print(plr_obj)
#> plR class object -- output from plR_read and valid input for plR_permute
#> NOTE:
#> Standard covariate columns not detected in obj.info
#> ** gene score deconfounding requires valid 'wt.mat' object (see ?polylinkR::plr_permute)
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> │ set.info │
#> └────────────────────────────────────────────────────────────────────────────────┘
#> setID setName setN
#> <int> <char> <int>
#> 1: 1 test_set_1 2
#> 2: 2 test_set_2 2
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> │ obj.info │
#> └────────────────────────────────────────────────────────────────────────────────┘
#> objID objStat objName chr startpos endpos startpos.base endpos.base
#> <int> <num> <char> <char> <int> <int> <int> <int>
#> 1: 1 0.5 gene1 chr1 1000 2000 1000 2000
#> 2: 2 0.6 gene2 chr1 3000 4000 3000 4000
#> 3: 3 0.7 gene3 chr1 5000 6000 5000 6000
#> midpos
#> <num>
#> 1: 1500
#> 2: 3500
#> 3: 5500
#> ┌────────────────────────────────────────────────────────────────────────────────┐
#> │ set.obj │
#> └────────────────────────────────────────────────────────────────────────────────┘
#> setID objID
#> <int> <int>
#> 1: 1 1
#> 2: 1 2
#> 3: 2 2
#> 4: 2 3The plR_read() function validates the input files and
creates a plR class object containing:
- set.info: Gene set information
- obj.info: Gene (object) information with scores
- set.obj: Gene-to-set mappings
Inspecting the plR Object
You can inspect the plR object using standard methods:
# Summary of the object
summary(plr_obj)
#> No tests performed. Nothing to summarise
#>
# Structure of the object
str(plr_obj)
#> List of 3
#> $ set.info:Classes 'data.table' and 'data.frame': 2 obs. of 3 variables:
#> ..$ setID : int [1:2] 1 2
#> ..$ setName: chr [1:2] "test_set_1" "test_set_2"
#> ..$ setN : int [1:2] 2 2
#> ..- attr(*, ".internal.selfref")=<externalptr>
#> $ obj.info:Classes 'data.table' and 'data.frame': 3 obs. of 9 variables:
#> ..$ objID : int [1:3] 1 2 3
#> ..$ objStat : num [1:3] 0.5 0.6 0.7
#> ..$ objName : chr [1:3] "gene1" "gene2" "gene3"
#> ..$ chr : chr [1:3] "chr1" "chr1" "chr1"
#> ..$ startpos : int [1:3] 1000 3000 5000
#> ..$ endpos : int [1:3] 2000 4000 6000
#> ..$ startpos.base: int [1:3] 1000 3000 5000
#> ..$ endpos.base : int [1:3] 2000 4000 6000
#> ..$ midpos : num [1:3] 1500 3500 5500
#> ..- attr(*, ".internal.selfref")=<externalptr>
#> $ set.obj :Classes 'data.table' and 'data.frame': 4 obs. of 2 variables:
#> ..$ setID: int [1:4] 1 1 2 2
#> ..$ objID: int [1:4] 1 2 2 3
#> ..- attr(*, ".internal.selfref")=<externalptr>
#> - attr(*, "plR.data")=List of 1
#> ..$ read.data:List of 12
#> .. ..$ n.genes : int 3
#> .. ..$ n.sets : int 2
#> .. ..$ n.chr : int 1
#> .. ..$ n.cov : num 0
#> .. ..$ n.set.genes: int 3
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#> .. ..$ pos.info : logi TRUE
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#> .. ..$ input.path : chr "/home/runner/work/_temp/Library/polylinkR/extdata/tiny_polylinkR"
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#> - attr(*, "plR.summary")=List of 1
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polylinkR requires three input files:
1. ObjInfo.txt (Gene Information)
Contains gene-level information:
- objID: Unique gene identifier
- objStat: Gene-level statistic (e.g., p-value, z-score)
- Optional columns: chr, startpos, endpos (for coordinate-based analysis)
Important Notes
Next Steps
After reading the data, you would typically proceed through the pipeline:
- plR_permute() - Generate permutation-based null distributions
- plR_rescale() - Account for genetic autocorrelation
- plR_prune() - Identify significant gene sets
See the function documentation (?plR_permute,
?plR_rescale, ?plR_prune) for details on
subsequent steps.
Session Information
sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] polylinkR_0.5.0
#>
#> loaded via a namespace (and not attached):
#> [1] dqrng_0.4.1 sass_0.4.10 future_1.70.0
#> [4] robustbase_0.99-7 distances_0.1.13 lattice_0.22-9
#> [7] listenv_0.10.1 digest_0.6.39 magrittr_2.0.5
#> [10] evaluate_1.0.5 grid_4.5.3 iterators_1.0.14
#> [13] fastmap_1.2.0 Matrix_1.7-4 foreach_1.5.2
#> [16] jsonlite_2.0.0 mgcv_1.9-4 codetools_0.2-20
#> [19] textshaping_1.0.5 jquerylib_0.1.4 cli_3.6.6
#> [22] rlang_1.2.0 zigg_0.0.2 parallelly_1.46.1
#> [25] future.apply_1.20.2 splines_4.5.3 intervals_0.15.5
#> [28] cachem_1.1.0 yaml_2.3.12 FNN_1.1.4.1
#> [31] tools_4.5.3 parallel_4.5.3 doFuture_1.2.1
#> [34] globals_0.19.1 Rfast_2.1.5.2 spacetime_1.3-3
#> [37] R6_2.6.1 zoo_1.8-15 lifecycle_1.0.5
#> [40] fs_2.0.1 ragg_1.5.2 pkgconfig_2.0.3
#> [43] desc_1.4.3 progressr_0.19.0 pkgdown_2.2.0
#> [46] RcppParallel_5.1.11-2 bslib_0.10.0 data.table_1.18.2.1
#> [49] Rcpp_1.1.1 gstat_2.1-6 systemfonts_1.3.2
#> [52] DEoptimR_1.1-4 xfun_0.57 knitr_1.51
#> [55] nlme_3.1-168 htmltools_0.5.9 igraph_2.2.3
#> [58] rmarkdown_2.31 xts_0.14.2 ismev_1.43
#> [61] compiler_4.5.3 sp_2.2-1