In this vignette, we demonstrate the unsegmented block bootstrap functionality implemented in nullranges. “Unsegmented” refers to the fact that this implementation does not consider segmentation of the genome for sampling of blocks, see the segmented block bootstrap vignette for the alternative implementation.

Timing on DHS peaks

First we use the DNase hypersensitivity peaks in A549 downloaded from AnnotationHub, and pre-processed as described in the nullrangesOldData package.

library(nullrangesData)
dhs <- DHSA549Hg38()
library(nullranges)

The following chunk of code evaluates various types of bootstrap/permutation schemes, first within chromosome, and then across chromosome (the default). The default type is bootstrap, and the default for withinChrom is FALSE (bootstrapping with blocks moving across chromosomes).

set.seed(5) # reproducibility
library(microbenchmark)
blockLength <- 5e5
microbenchmark(
  list=alist(
    p_within=bootRanges(dhs, blockLength=blockLength,
                        type="permute", withinChrom=TRUE),
    b_within=bootRanges(dhs, blockLength=blockLength,
                        type="bootstrap", withinChrom=TRUE),
    p_across=bootRanges(dhs, blockLength=blockLength,
                        type="permute", withinChrom=FALSE),
    b_across=bootRanges(dhs, blockLength=blockLength,
                        type="bootstrap", withinChrom=FALSE)
  ), times=10)
## Unit: milliseconds
##      expr       min        lq      mean    median        uq       max neval cld
##  p_within 2284.3076 2359.4733 3079.9106 3011.9171 3677.2982 4149.8073    10   b
##  b_within 2095.1891 2175.5050 2961.3093 3164.2068 3593.9749 3783.3100    10   b
##  p_across  684.7402  726.9035  787.1269  776.3064  833.4356  943.1689    10  a 
##  b_across  632.1611  804.3860 1060.1851  896.5106  924.6337 3020.8486    10  a

Visualize on synthetic data

We create some synthetic ranges in order to visualize the different options of the unsegmented bootstrap implemented in nullranges.

library(GenomicRanges)
seq_nms <- rep(c("chr1","chr2","chr3"),c(4,5,2))
gr <- GRanges(seqnames=seq_nms,
              IRanges(start=c(1,101,121,201,
                              101,201,216,231,401,
                              1,101),
                      width=c(20, 5, 5, 30,
                              20, 5, 5, 5, 30,
                              80, 40)),
              seqlengths=c(chr1=300,chr2=450,chr3=200),
              chr=factor(seq_nms))

The following function uses functionality from plotgardener to plot the ranges. Note in the plotting helper function that chr will be used to color ranges by chromosome of origin.

suppressPackageStartupMessages(library(plotgardener))
plotGRanges <- function(gr) {
  pageCreate(width = 5, height = 2, xgrid = 0,
                ygrid = 0, showGuides = FALSE)
  for (i in seq_along(seqlevels(gr))) {
    chrom <- seqlevels(gr)[i]
    chromend <- seqlengths(gr)[[chrom]]
    suppressMessages({
      p <- pgParams(chromstart = 0, chromend = chromend,
                    x = 0.5, width = 4*chromend/500, height = 0.5,
                    at = seq(0, chromend, 50),
                    fill = colorby("chr", palette=palette.colors))
      prngs <- plotRanges(data = gr, params = p,
                          chrom = chrom,
                          y = 0.25 + (i-1)*.7,
                          just = c("left", "bottom"))
      annoGenomeLabel(plot = prngs, params = p, y = 0.30 + (i-1)*.7)
    })
  }
}
plotGRanges(gr)

Within chromosome

Visualizing two permutations of blocks within chromosome:

for (i in 1:2) {
  gr_prime <- bootRanges(gr, blockLength=100, type="permute", withinChrom=TRUE)
  plotGRanges(gr_prime)
}

Visualizing two bootstraps within chromosome:

for (i in 1:2) {
  gr_prime <- bootRanges(gr, blockLength=100, withinChrom=TRUE)
  plotGRanges(gr_prime)
}

Across chromosome

Visualizing two permutations of blocks across chromosome. Here we use larger blocks than previously.

for (i in 1:2) {
  gr_prime <- bootRanges(gr, blockLength=200, type="permute", withinChrom=FALSE)
  plotGRanges(gr_prime)
}

Visualizing two bootstraps across chromosome:

for (i in 1:2) {
  gr_prime <- bootRanges(gr, blockLength=200, withinChrom=FALSE)
  plotGRanges(gr_prime)
}

Session information

sessionInfo()
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] microbenchmark_1.4.9        tidyr_1.2.0                
##  [3] EnsDb.Hsapiens.v86_2.99.0   ensembldb_2.20.0           
##  [5] AnnotationFilter_1.20.0     GenomicFeatures_1.48.0     
##  [7] AnnotationDbi_1.58.0        patchwork_1.1.1            
##  [9] plyranges_1.16.0            nullrangesData_1.1.1       
## [11] ExperimentHub_2.4.0         AnnotationHub_3.4.0        
## [13] BiocFileCache_2.4.0         dbplyr_2.1.1               
## [15] ggplot2_3.3.5               plotgardener_1.2.0         
## [17] nullranges_1.2.0            InteractionSet_1.24.0      
## [19] SummarizedExperiment_1.26.0 Biobase_2.56.0             
## [21] MatrixGenerics_1.8.0        matrixStats_0.62.0         
## [23] GenomicRanges_1.48.0        GenomeInfoDb_1.32.0        
## [25] IRanges_2.30.0              S4Vectors_0.34.0           
## [27] BiocGenerics_0.42.0        
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.7                    RcppHMM_1.2.2                
##   [3] lazyeval_0.2.2                splines_4.2.0                
##   [5] BiocParallel_1.30.0           TH.data_1.1-1                
##   [7] digest_0.6.29                 yulab.utils_0.0.4            
##   [9] htmltools_0.5.2               fansi_1.0.3                  
##  [11] magrittr_2.0.3                memoise_2.0.1                
##  [13] ks_1.13.5                     Biostrings_2.64.0            
##  [15] sandwich_3.0-1                prettyunits_1.1.1            
##  [17] jpeg_0.1-9                    colorspace_2.0-3             
##  [19] blob_1.2.3                    rappdirs_0.3.3               
##  [21] xfun_0.30                     dplyr_1.0.8                  
##  [23] crayon_1.5.1                  RCurl_1.98-1.6               
##  [25] jsonlite_1.8.0                survival_3.3-1               
##  [27] zoo_1.8-10                    glue_1.6.2                   
##  [29] gtable_0.3.0                  zlibbioc_1.42.0              
##  [31] XVector_0.36.0                strawr_0.0.9                 
##  [33] DelayedArray_0.22.0           scales_1.2.0                 
##  [35] mvtnorm_1.1-3                 DBI_1.1.2                    
##  [37] Rcpp_1.0.8.3                  xtable_1.8-4                 
##  [39] progress_1.2.2                gridGraphics_0.5-1           
##  [41] bit_4.0.4                     mclust_5.4.9                 
##  [43] httr_1.4.2                    RColorBrewer_1.1-3           
##  [45] speedglm_0.3-4                ellipsis_0.3.2               
##  [47] pkgconfig_2.0.3               XML_3.99-0.9                 
##  [49] farver_2.1.0                  sass_0.4.1                   
##  [51] utf8_1.2.2                    DNAcopy_1.70.0               
##  [53] ggplotify_0.1.0               tidyselect_1.1.2             
##  [55] labeling_0.4.2                rlang_1.0.2                  
##  [57] later_1.3.0                   munsell_0.5.0                
##  [59] BiocVersion_3.15.2            tools_4.2.0                  
##  [61] cachem_1.0.6                  cli_3.3.0                    
##  [63] generics_0.1.2                RSQLite_2.2.12               
##  [65] ggridges_0.5.3                evaluate_0.15                
##  [67] stringr_1.4.0                 fastmap_1.1.0                
##  [69] yaml_2.3.5                    knitr_1.38                   
##  [71] bit64_4.0.5                   purrr_0.3.4                  
##  [73] KEGGREST_1.36.0               mime_0.12                    
##  [75] pracma_2.3.8                  xml2_1.3.3                   
##  [77] biomaRt_2.52.0                compiler_4.2.0               
##  [79] filelock_1.0.2                curl_4.3.2                   
##  [81] png_0.1-7                     interactiveDisplayBase_1.34.0
##  [83] tibble_3.1.6                  bslib_0.3.1                  
##  [85] stringi_1.7.6                 highr_0.9                    
##  [87] lattice_0.20-45               ProtGenerics_1.28.0          
##  [89] Matrix_1.4-1                  vctrs_0.4.1                  
##  [91] pillar_1.7.0                  lifecycle_1.0.1              
##  [93] BiocManager_1.30.17           jquerylib_0.1.4              
##  [95] data.table_1.14.2             bitops_1.0-7                 
##  [97] httpuv_1.6.5                  rtracklayer_1.56.0           
##  [99] R6_2.5.1                      BiocIO_1.6.0                 
## [101] promises_1.2.0.1              KernSmooth_2.23-20           
## [103] codetools_0.2-18              MASS_7.3-57                  
## [105] assertthat_0.2.1              rjson_0.2.21                 
## [107] withr_2.5.0                   GenomicAlignments_1.32.0     
## [109] Rsamtools_2.12.0              multcomp_1.4-19              
## [111] GenomeInfoDbData_1.2.8        parallel_4.2.0               
## [113] hms_1.1.1                     rmarkdown_2.14               
## [115] shiny_1.7.1                   restfulr_0.0.13