# Unsegmented block bootstrap

#### Michael Love

#### 11/07/2021

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()

    ## see ?nullrangesData and browseVignettes('nullrangesData') for documentation

    ## loading from cache

    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 887.4291 898.3263 913.1266 912.1992 925.2197 936.0994    10   b
    ##  b_within 730.2927 763.8793 786.0249 789.2803 813.4819 824.1041    10   b
    ##  p_across 176.6687 182.4015 273.6263 210.9139 222.5724 894.3762    10  a
    ##  b_across 194.3487 203.5147 220.4379 226.5206 230.3348 243.7533    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 4.1.1 (2021-08-10)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server x64 (build 17763)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## 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
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    ##  [87] Matrix_1.3-4                  vctrs_0.3.8
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    ## [107] Rsamtools_2.10.0              multcomp_1.4-17
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