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PlinkMatrix: DelayedArray interface to plink bed-type files

Vincent J. Carey, stvjc at channing.harvard.edu

March 15, 2026

Source: vignettes/PlinkMatrix.Rmd
PlinkMatrix.Rmd

Introduction

Many genetic studies employ PLINK for data management and analysis.

This package facilitates interrogation of PLINK bed files through the Bioconductor DelayedArray protocol

Demonstration

The example_PlinkMatrix function will

  • retrieve a zip archive from a cloud store if needed, and place it in the default BiocFileCache cache
  • unzip the cached archive to bed, bim, fam files
  • create the DelayedArray interface to the unzipped archive

The example data are those distributed with tensorQTL, transformed to bed using plink2.

library(PlinkMatrix)
gdemo = example_PlinkMatrix()
colnames(gdemo) = gsub("0_", "", colnames(gdemo))
gdemo
## <367759 x 445> DelayedMatrix object of type "double":
##                         HG00096 HG00097 HG00099 ... NA20826 NA20828
##     chr18_10644_C_G_b38       0       0       0   .       0       0
##     chr18_10847_C_A_b38       0       0       0   .       0       0
##     chr18_11275_G_A_b38       0       0       0   .       0       0
##     chr18_11358_G_A_b38       0       0       0   .       0       0
##     chr18_11445_G_A_b38       0       0       0   .       0       0
##                     ...       .       .       .   .       .       .
## chr18_80259028_AG_A_b38       1       2       2   .       0       1
##  chr18_80259147_G_C_b38       0       0       0   .       0       0
##  chr18_80259181_A_G_b38       0       0       0   .       0       0
##  chr18_80259190_C_G_b38       1       0       0   .       1       0
##  chr18_80259245_C_A_b38       0       0       0   .       0       0

Sanity check

We will use ancestry information and approximate PCA to illustrate plausibility of the approach used here.

We have codes for the ancestral origins of samples.

data(g445samples)
table(g445samples$Population.code)
## 
## CEU FIN GBR TSI YRI 
##  89  92  86  91  87

We’ll take a random sample of 1000 SNP and retrieve 10 PCs, then visualize aspects of the reexpression to PCs for discriminating population of ancestry.

library(irlba)
set.seed(1234)
ss = sort(sample(seq_len(nrow(gdemo)), size=1000))
pca = prcomp_irlba(t(gdemo[ss,]),10)
pairs(pca$x[,1:4], col=factor(g445samples$Population.code), pch=19, cex=.5)

boxplot(split(pca$x[,2]+pca$x[,1], g445samples$Population.code))

SummarizedExperiment wrapper; subsetting with GenomicRanges 

data(example_GRanges)
library(SummarizedExperiment)
nse = SummarizedExperiment(list(genotypes=gdemo), 
   rowData=example_GRanges, colData=g445samples)
nse
## class: RangedSummarizedExperiment 
## dim: 367759 445 
## metadata(0):
## assays(1): genotypes
## rownames(367759): chr18_10644_C_G_b38 chr18_10847_C_A_b38 ...
##   chr18_80259190_C_G_b38 chr18_80259245_C_A_b38
## rowData names(0):
## colnames(445): HG00096 HG00097 ... NA20826 NA20828
## colData names(5): Sex Population.code Population.name
##   Superpopulation.code Superpopulation.name
little = GenomicRanges::GRanges("chr18:1-100000")
subsetByOverlaps(nse, little)
## class: RangedSummarizedExperiment 
## dim: 353 445 
## metadata(0):
## assays(1): genotypes
## rownames(353): chr18_10644_C_G_b38 chr18_10847_C_A_b38 ...
##   chr18_99977_C_T_b38 chr18_99998_G_A_b38
## rowData names(0):
## colnames(445): HG00096 HG00097 ... NA20826 NA20828
## colData names(5): Sex Population.code Population.name
##   Superpopulation.code Superpopulation.name

Session information 

## R version 4.5.2 (2025-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.7.4
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] irlba_2.3.7                 PlinkMatrix_0.99.0         
##  [3] SummarizedExperiment_1.40.0 Biobase_2.70.0             
##  [5] GenomicRanges_1.62.1        Seqinfo_1.0.0              
##  [7] DelayedArray_0.36.0         SparseArray_1.10.8         
##  [9] S4Arrays_1.10.1             abind_1.4-8                
## [11] IRanges_2.44.0              S4Vectors_0.48.0           
## [13] MatrixGenerics_1.22.0       matrixStats_1.5.0          
## [15] BiocGenerics_0.56.0         generics_0.1.4             
## [17] Matrix_1.7-4                Rcpp_1.1.1                 
## [19] BiocStyle_2.38.0           
## 
## loaded via a namespace (and not attached):
##  [1] xfun_0.56           bslib_0.10.0        httr2_1.2.2        
##  [4] htmlwidgets_1.6.4   lattice_0.22-9      vctrs_0.7.1        
##  [7] tools_4.5.2         curl_7.0.0          tibble_3.3.1       
## [10] RSQLite_2.4.6       blob_1.3.0          pkgconfig_2.0.3    
## [13] dbplyr_2.5.2        desc_1.4.3          lifecycle_1.0.5    
## [16] compiler_4.5.2      textshaping_1.0.4   htmltools_0.5.9    
## [19] sass_0.4.10         yaml_2.3.12         pkgdown_2.2.0      
## [22] pillar_1.11.1       jquerylib_0.1.4     cachem_1.1.0       
## [25] tidyselect_1.2.1    digest_0.6.39       dplyr_1.2.0        
## [28] purrr_1.2.1         bookdown_0.46       fastmap_1.2.0      
## [31] grid_4.5.2          cli_3.6.5           magrittr_2.0.4     
## [34] withr_3.0.2         filelock_1.0.3      rappdirs_0.3.4     
## [37] bit64_4.6.0-1       rmarkdown_2.30      XVector_0.50.0     
## [40] bit_4.6.0           otel_0.2.0          ragg_1.5.0         
## [43] memoise_2.0.1       evaluate_1.0.5      knitr_1.51         
## [46] BiocFileCache_3.0.0 rlang_1.1.7         glue_1.8.0         
## [49] DBI_1.3.0           BiocManager_1.30.27 jsonlite_2.0.0     
## [52] R6_2.6.1            systemfonts_1.3.1   fs_1.6.6