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.
## see ?nullrangesData and browseVignettes('nullrangesData') for documentation
## loading from cache
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 1820.2076 2113.9483 2743.9982 2166.5210 4005.6536 4351.6155 10 b
## b_within 1530.0095 1814.9135 2017.7530 1956.0643 2029.4123 3255.2062 10 b
## p_across 391.5507 424.5649 537.8631 475.8006 551.6568 841.8597 10 a
## b_across 407.7798 435.0697 687.8482 467.5030 502.6434 2401.8366 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 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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_1.4.8 excluderanges_0.99.6
## [3] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.1
## [5] AnnotationFilter_1.18.0 GenomicFeatures_1.46.1
## [7] AnnotationDbi_1.56.1 patchwork_1.1.1
## [9] plyranges_1.14.0 nullrangesData_1.0.0
## [11] ExperimentHub_2.2.0 AnnotationHub_3.2.0
## [13] BiocFileCache_2.2.0 dbplyr_2.1.1
## [15] ggplot2_3.3.5 plotgardener_1.0.1
## [17] nullranges_1.0.1 InteractionSet_1.22.0
## [19] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [21] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [23] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [25] IRanges_2.28.0 S4Vectors_0.32.2
## [27] BiocGenerics_0.40.0
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 RcppHMM_1.2.2
## [3] lazyeval_0.2.2 splines_4.1.1
## [5] BiocParallel_1.28.0 TH.data_1.1-0
## [7] digest_0.6.28 yulab.utils_0.0.4
## [9] htmltools_0.5.2 fansi_0.5.0
## [11] magrittr_2.0.1 memoise_2.0.0
## [13] ks_1.13.2 Biostrings_2.62.0
## [15] sandwich_3.0-1 prettyunits_1.1.1
## [17] colorspace_2.0-2 blob_1.2.2
## [19] rappdirs_0.3.3 xfun_0.28
## [21] dplyr_1.0.7 crayon_1.4.2
## [23] RCurl_1.98-1.5 jsonlite_1.7.2
## [25] survival_3.2-13 zoo_1.8-9
## [27] glue_1.5.0 gtable_0.3.0
## [29] zlibbioc_1.40.0 XVector_0.34.0
## [31] strawr_0.0.9 DelayedArray_0.20.0
## [33] scales_1.1.1 mvtnorm_1.1-3
## [35] DBI_1.1.1 Rcpp_1.0.7
## [37] xtable_1.8-4 progress_1.2.2
## [39] gridGraphics_0.5-1 bit_4.0.4
## [41] mclust_5.4.8 httr_1.4.2
## [43] RColorBrewer_1.1-2 speedglm_0.3-3
## [45] ellipsis_0.3.2 pkgconfig_2.0.3
## [47] XML_3.99-0.8 farver_2.1.0
## [49] sass_0.4.0 utf8_1.2.2
## [51] DNAcopy_1.68.0 ggplotify_0.1.0
## [53] tidyselect_1.1.1 labeling_0.4.2
## [55] rlang_0.4.12 later_1.3.0
## [57] munsell_0.5.0 BiocVersion_3.14.0
## [59] tools_4.1.1 cachem_1.0.6
## [61] generics_0.1.1 RSQLite_2.2.8
## [63] ggridges_0.5.3 evaluate_0.14
## [65] stringr_1.4.0 fastmap_1.1.0
## [67] yaml_2.2.1 knitr_1.36
## [69] bit64_4.0.5 purrr_0.3.4
## [71] KEGGREST_1.34.0 mime_0.12
## [73] pracma_2.3.3 xml2_1.3.2
## [75] biomaRt_2.50.0 compiler_4.1.1
## [77] filelock_1.0.2 curl_4.3.2
## [79] png_0.1-7 interactiveDisplayBase_1.32.0
## [81] tibble_3.1.6 bslib_0.3.1
## [83] stringi_1.7.5 highr_0.9
## [85] lattice_0.20-45 ProtGenerics_1.26.0
## [87] Matrix_1.3-4 vctrs_0.3.8
## [89] pillar_1.6.4 lifecycle_1.0.1
## [91] BiocManager_1.30.16 jquerylib_0.1.4
## [93] data.table_1.14.2 bitops_1.0-7
## [95] httpuv_1.6.3 rtracklayer_1.54.0
## [97] R6_2.5.1 BiocIO_1.4.0
## [99] promises_1.2.0.1 KernSmooth_2.23-20
## [101] codetools_0.2-18 MASS_7.3-54
## [103] assertthat_0.2.1 rjson_0.2.20
## [105] withr_2.4.2 GenomicAlignments_1.30.0
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