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.
First we use the DNase hypersensitivity peaks in A549 downloaded from AnnotationHub, and pre-processed as described in the nullrangesOldData package.
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
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)
})
}
}
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)
}
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)
}
## 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
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## [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
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## [35] mvtnorm_1.1-3 DBI_1.1.2
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## [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
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## [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
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## [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
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