Unsegmented block bootstrap
Michael Love
04/26/2022
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 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 868.6567 1110.0724 1628.7964 1934.1300 2045.7860 2176.7684 10 c
## b_within 764.3811 971.3335 1221.7902 1112.8045 1418.2535 1974.4171 10 b
## p_across 211.3763 238.6568 285.8582 267.4975 307.0418 417.0240 10 a
## b_across 205.5775 232.7860 302.1538 248.4448 303.6542 528.2687 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 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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 tidyr_1.2.0
## [3] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.21.0
## [5] AnnotationFilter_1.21.0 GenomicFeatures_1.49.0
## [7] AnnotationDbi_1.59.0 patchwork_1.1.1
## [9] plyranges_1.17.0 nullrangesData_1.1.1
## [11] ExperimentHub_2.5.0 AnnotationHub_3.5.0
## [13] BiocFileCache_2.5.0 dbplyr_2.1.1
## [15] ggplot2_3.3.5 plotgardener_1.3.0
## [17] nullranges_1.3.0 InteractionSet_1.25.0
## [19] SummarizedExperiment_1.27.0 Biobase_2.57.0
## [21] MatrixGenerics_1.9.0 matrixStats_0.62.0
## [23] GenomicRanges_1.49.0 GenomeInfoDb_1.33.0
## [25] IRanges_2.31.0 S4Vectors_0.35.0
## [27] BiocGenerics_0.43.0
##
## 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_1.31.0 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_2.65.0
## [15] sandwich_3.0-1 prettyunits_1.1.1
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## [19] blob_1.2.3 rappdirs_0.3.3
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## [51] utf8_1.2.2 DNAcopy_1.71.0
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## [83] tibble_3.1.6 bslib_0.3.1
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## [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_1.57.0
## [99] R6_2.5.1 BiocIO_1.7.0
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## [103] codetools_0.2-18 MASS_7.3-57
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