Unsegmented block bootstrap
Michael Love
11/01/2022
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 nullrangesData 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 933.3921 960.8329 1360.1196 1167.8408 1935.4512 2163.3598 10 b
## b_within 852.4478 942.3608 1340.6979 947.0155 1221.6593 3875.1583 10 b
## p_across 214.8735 231.0009 280.1609 255.0290 296.8121 487.2238 10 a
## b_across 238.9146 247.7053 263.8726 259.3734 278.3249 313.9127 10 aVisualize 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)
})
}
}
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
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] microbenchmark_1.4.9 purrr_0.3.5
## [3] ggridges_0.5.4 tidyr_1.2.1
## [5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.22.0
## [7] AnnotationFilter_1.22.0 GenomicFeatures_1.50.0
## [9] AnnotationDbi_1.60.0 patchwork_1.1.2
## [11] plyranges_1.18.0 nullrangesData_1.3.0
## [13] ExperimentHub_2.6.0 AnnotationHub_3.6.0
## [15] BiocFileCache_2.6.0 dbplyr_2.2.1
## [17] ggplot2_3.3.6 plotgardener_1.4.0
## [19] nullranges_1.4.0 InteractionSet_1.26.0
## [21] SummarizedExperiment_1.28.0 Biobase_2.58.0
## [23] MatrixGenerics_1.10.0 matrixStats_0.62.0
## [25] GenomicRanges_1.50.0 GenomeInfoDb_1.34.0
## [27] IRanges_2.32.0 S4Vectors_0.36.0
## [29] BiocGenerics_0.44.0
##
## loaded via a namespace (and not attached):
## [1] RcppHMM_1.2.2 lazyeval_0.2.2
## [3] splines_4.2.1 BiocParallel_1.32.0
## [5] TH.data_1.1-1 digest_0.6.30
## [7] yulab.utils_0.0.5 htmltools_0.5.3
## [9] fansi_1.0.3 magrittr_2.0.3
## [11] memoise_2.0.1 ks_1.13.5
## [13] Biostrings_2.66.0 sandwich_3.0-2
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## [53] tidyselect_1.2.0 labeling_0.4.2
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## [91] jquerylib_0.1.4 data.table_1.14.4
## [93] bitops_1.0-7 httpuv_1.6.6
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## [101] MASS_7.3-58.1 assertthat_0.2.1
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