## ----setup--------------------------------------------------------------------
knitr::opts_chunk$set(message = FALSE, crop = NULL)

## ----load-packages, eval = TRUE-----------------------------------------------
library(tidyverse)
library(Rsamtools)
library(csaw)
library(BiocParallel)
library(rtracklayer)
library(edgeR)
library(patchwork)
library(extraChIPs)
library(plyranges)
library(scales)
library(glue)
library(here)
library(magrittr)
library(quantro)
library(ggrepel)
theme_set(theme_bw())

## ----samples, eval = FALSE----------------------------------------------------
#  samples <- here("data", "PRJNA509779.tsv") %>%
#    read_tsv() %>%
#    dplyr::filter(target == "H3K27ac") %>%
#    mutate(treatment = factor(treatment, levels = c("E2", "E2DHT")))
#  accessions <- unique(c(samples$accession, samples$input))
#  treat_colours <- c("steelblue", "red3", "grey")
#  names(treat_colours) <- c(levels(samples$treatment), "Input")

## ----bfl, eval = FALSE--------------------------------------------------------
#  bfl <- here("data", "H3K27ac", glue("{accessions}.bam")) %>%
#    BamFileList() %>%
#    setNames(str_remove_all(names(.), ".bam"))
#  file.exists(path(bfl))

## ----sq-----------------------------------------------------------------------
sq <- defineSeqinfo("GRCh37")

## ----wincounts, eval = FALSE--------------------------------------------------
#  greylist <- here("data", "chr10_greylist.bed") %>%
#    import.bed(seqinfo = sq)
#  blacklist <- here("data", "chr10_blacklist.bed") %>%
#    import.bed(seqinfo = sq)
#  rp <- readParam(
#    pe = "none",
#    dedup = TRUE,
#    restrict = "chr10",
#    discard = c(greylist, blacklist)
#  )
#  wincounts <- windowCounts(
#    bam.files = bfl,
#    spacing = 40,
#    width = 120,
#    ext = 200,
#    filter = length(bfl),
#    param = rp
#  )
#  seqlevels(wincounts) <- seqlevels(sq)
#  seqinfo(wincounts) <- sq

## ----rowranges-wincounts, eval = FALSE----------------------------------------
#  rowRanges(wincounts)

## ----coldata-wincounts, eval = FALSE------------------------------------------
#  colData(wincounts)

## ----update-coldata, eval = FALSE---------------------------------------------
#  colData(wincounts) <- colData(wincounts) %>%
#    as_tibble(rownames = "accession") %>%
#    left_join(samples, by = "accession") %>%
#    dplyr::select(
#      accession, all_of(colnames(colData(wincounts))), target, treatment
#    ) %>%
#    mutate(
#      target = str_replace_na(target, "Input"),
#      treatment = str_replace_na(treatment, "Input") %>%
#        as.factor()
#    ) %>%
#    DataFrame(row.names = .$accession)

## ----plot-densities, eval = FALSE---------------------------------------------
#  plotAssayDensities(wincounts, colour = "treatment", trans = "log1p") +
#    theme_bw() +
#    scale_colour_manual(values = treat_colours)

## ----filter-counts, eval = FALSE----------------------------------------------
#  guide_regions <- here("data", "H3K27ac", "H3K27ac_chr10.bed") %>%
#    import.bed(seqinfo = sq)
#  filtcounts <- dualFilter(
#    x = wincounts, bg = "SRR8315192", ref = guide_regions, q = 0.6
#  )

## ----plot-density-pca, eval = FALSE-------------------------------------------
#  A <- plotAssayDensities(filtcounts, assay = "logCPM", colour = "treatment") +
#    scale_colour_manual(values = treat_colours) +
#    theme_bw()
#  B <- plotAssayPCA(filtcounts, "logCPM", colour = "treatment", label = "accession") +
#    scale_colour_manual(values = treat_colours) +
#    theme_bw()
#  A + B +
#    plot_layout(guides = "collect") + plot_annotation(tag_levels = "A")

## ----add-dens-pca, echo = FALSE, eval = TRUE, fig.cap = "*A) Densities for logCPM values across all samples after discarding windows less likely to contain H3K27ac signal using `dualFilter()` B) PCA plot based on the logCPM assay*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-density-pca-1.png")

## ----plot-assay-rle, eval = FALSE---------------------------------------------
#  a <- plotAssayRle(filtcounts, assay = "logCPM", fill = "treat") +
#    geom_hline(yintercept = 0, linetype = 2, colour = "grey") +
#    scale_fill_manual(values = treat_colours) +
#    ggtitle("RLE: Across All Samples") +
#    theme_bw()
#  b <- plotAssayRle(
#    filtcounts, assay = "logCPM", fill = "treat", rle_group = "treat"
#  ) +
#    geom_hline(yintercept = 0, linetype = 2, colour = "grey") +
#    scale_fill_manual(values = treat_colours) +
#    ggtitle("RLE: Within Treatment Groups") +
#    theme_bw()
#  a + b + plot_layout(guides = "collect") +
#    plot_annotation(tag_levels = "A")

## ----add-rle-plot, eval = TRUE, echo = FALSE, fig.cap = "*RLE plots (A) across all samples and (B) with values calculated within treatment groups*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-assay-rle-1.png")

## ----fit-gr, eval = FALSE-----------------------------------------------------
#  X <- model.matrix(~treatment, data = colData(filtcounts))
#  fit_gr <- fitAssayDiff(filtcounts, design = X, fc = 1.2, asRanges = TRUE)

## ----results-gr, eval = FALSE-------------------------------------------------
#  results_gr <- mergeByHMP(fit_gr, inc_cols = "overlaps_ref", merge_within = 120) %>%
#    addDiffStatus(drop = TRUE)
#  arrange(results_gr, hmp)[1:5]

## ----plot-ma-libsize, eval = FALSE--------------------------------------------
#  A <- fit_gr %>%
#    as_tibble() %>%
#    ggplot(aes(logCPM, logFC)) +
#    geom_point(alpha = 0.6) +
#    geom_smooth(se = FALSE, method = "loess") +
#    geom_label(
#      aes(label = label),
#      data = . %>%
#        summarise(
#          n = dplyr::n(),
#          logCPM = max(logCPM) - 0.15 * diff(range(logCPM)),
#          logFC = max(logFC) - 0.05 * diff(range(logFC)),
#          label = glue("{comma(n)}\nSliding Windows")
#        )
#    ) +
#    ylim(range(fit_gr$logFC)) +
#    ggtitle("MA Plot: All Retained Sliding Windows")
#  B <- results_gr %>%
#    as_tibble() %>%
#    mutate(`FDR < 0.05` = hmp_fdr < 0.05) %>%
#    ggplot(aes(logCPM, logFC)) +
#    geom_point(aes(colour = `FDR < 0.05`), alpha = 0.6) +
#    geom_smooth(se = FALSE, method = "loess") +
#    geom_label_repel(
#      aes(label = range, colour = `FDR < 0.05`),
#      data = . %>% dplyr::filter(hmp_fdr == min(hmp_fdr)),
#      show.legend = FALSE
#    ) +
#    geom_label(
#      aes(label = label),
#      data = . %>%
#        summarise(
#          n = dplyr::n(),
#          logCPM = max(logCPM) - 0.15 * diff(range(logCPM)),
#          logFC = max(fit_gr$logFC) - 0.05 * diff(range(fit_gr$logFC)),
#          label = glue("{comma(n)}\nMerged Windows")
#        )
#    ) +
#    scale_colour_manual(values = c("black", "red")) +
#    ylim(range(fit_gr$logFC)) +
#    ggtitle("MA Plot: Merged Windows")
#  A + B + plot_annotation(tag_levels = "A")

## ----add-ma-plot, echo=FALSE, eval=TRUE, fig.cap="*MA Plots using A) all sliding windows (after filtering before merging) and B) the final set of merged windows. The most highly ranked merged window is also labelled in the set of merged windows. Blue lines represent a loess curve through the data.*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-ma-libsize-1.png")

## ----qtest, eval = FALSE------------------------------------------------------
#  set.seed(100)
#  qtest <- assay(filtcounts, "counts") %>%
#    quantro(groupFactor = filtcounts$treatment, B = 1e3)
#  qtest

## ----fit-tmm, eval = FALSE----------------------------------------------------
#  tmm_gr <- fitAssayDiff(
#    filtcounts, design = X, fc = 1.2, norm = "TMM", asRanges = TRUE
#  )
#  tmm_results <- mergeByHMP(tmm_gr, inc_cols = "overlaps_ref", merge_within = 120) %>%
#    addDiffStatus(drop = TRUE)
#  table(tmm_results$status)

## ----plot-ma-tmm, eval = FALSE------------------------------------------------
#  tmm_gr %>%
#    as_tibble() %>%
#    ggplot(aes(logCPM, logFC)) +
#    geom_point(alpha = 0.6) +
#    geom_smooth(se = FALSE, method = "loess") +
#    ggtitle("MA Plot: TMM Normalisation")

## ----gencode, eval = FALSE----------------------------------------------------
#  gencode <- here("data/gencode.v43lift37.chr10.annotation.gtf.gz") %>%
#    import.gff() %>%
#    filter_by_overlaps(GRanges("chr10:42354900-100000000")) %>%
#    split(.$type)
#  seqlevels(gencode) <- seqlevels(sq)
#  seqinfo(gencode) <- sq

## ----tss, eval = FALSE--------------------------------------------------------
#  tss <- gencode$transcript %>%
#    resize(width = 1, fix = "start") %>%
#    select(gene_id, ends_with("name")) %>%
#    reduceMC(min.gapwidth = 0)

## ----subset-tss, eval = FALSE-------------------------------------------------
#  subset(tss, vapply(gene_name, function(x) "PPIF" %in% x, logical(1)))

## ----overlaps-any, eval = FALSE-----------------------------------------------
#  tmm_results$tss <- overlapsAny(tmm_results, tss)

## ----promoters, eval = FALSE--------------------------------------------------
#  promoters <- gencode$transcript %>%
#      select(gene_id, ends_with("name")) %>%
#      promoters(upstream = 2500, downstream = 500) %>%
#      reduceMC(simplify = FALSE)
#  tail(promoters)

## ----map-by-feature, eval = FALSE---------------------------------------------
#  tmm_results <- mapByFeature(
#    tmm_results, genes = gencode$gene,
#    prom = select(promoters, gene_id, gene_name)
#  )
#  tmm_results %>% filter(hmp_fdr < 0.05) %>% arrange(hmp)

## ----plot-ma-mapped, eval = FALSE---------------------------------------------
#  status_colours <- c(Unchanged = "grey70", Increased = "red3", Decreased = "royalblue")
#  tmm_results %>%
#    as_tibble() %>%
#    ggplot(aes(logCPM, logFC, colour = status, shape = tss)) +
#    geom_point() +
#    geom_label_repel(
#      aes(label = label),
#      data = . %>%
#        arrange(hmp) %>%
#        dplyr::slice(1:20) %>%
#        mutate(
#          label  = vapply(gene_name, paste, character(1), collapse = "; ") %>%
#            str_trunc(30)
#        ),
#      fill = "white", alpha = 0.6, show.legend = FALSE
#    ) +
#    scale_colour_manual(values = status_colours) +
#    scale_shape_manual(values = c(19, 21))

## ----add-ma-mapped-plot, eval = TRUE, echo = FALSE, fig.cap = "*MA-plot with the top 20 regions by significance labelled according to the most likely gene influenced by the detected signal. Regions which directly overlap a TSS are shown as empty circles*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-ma-mapped-1.png")

## ----define-regions, eval = FALSE---------------------------------------------
#  regions <- defineRegions(
#    genes = gencode$gene, transcripts = gencode$transcript, exons = gencode$exon
#  )
#  regions

## ----bet-overlap, eval = FALSE------------------------------------------------
#  tmm_results$bestOverlap <- bestOverlap(tmm_results, regions) %>%
#    factor(levels = names(regions))
#  filter(tmm_results, hmp_fdr < 0.05, bestOverlap == "promoter")

## ----reg-as-factor, eval = FALSE----------------------------------------------
#  reg_levels <- vapply(regions, function(x) x$region[1], character(1))
#  tmm_results$bestOverlap <- tmm_results %>%
#    bestOverlap(unlist(regions), var = "region") %>%
#    factor(levels = reg_levels)

## ----reg-colours, eval = FALSE------------------------------------------------
#  region_colours <- hcl.colors(length(reg_levels), "Viridis", rev = TRUE)
#  names(region_colours) <- reg_levels

## ----plot-pie-labels, eval = FALSE--------------------------------------------
#  plotPie(
#    tmm_results, fill = "bestOverlap", min_p = 0.05,
#    cat_glue = "{.data[[fill]]}\n{n}\n({percent(p, 0.1)})"
#  ) +
#    scale_fill_manual(values = region_colours)

## ----add-pit, eval = TRUE, echo = FALSE, fig.cap = "*Pie chart showing customised labels. Here the regions have been modified to use title case in the labels, but without modifying the underlying data.*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-pie-labels-1.png")

## ----plot-donut-default, eval = FALSE-----------------------------------------
#  plotSplitDonut(
#    tmm_results, inner = "bestOverlap", outer = "status",
#    inner_palette = region_colours
#  )

## ----add-donut, eval = TRUE, echo=FALSE, fig.cap = "*Split-Donut chart showing status with overlapping region*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-donut-default-1.png")

## ----plot-donut-customised, eval = FALSE--------------------------------------
#  plotSplitDonut(
#    tmm_results, inner = "bestOverlap", outer = "status",
#    inner_palette = region_colours, outer_palette = status_colours,
#    inner_glue = "{str_replace_all(.data[[inner]], ' ', '\n')}\nn = {comma(n)}\n{percent(p,0.1)}",
#    outer_glue = "{.data[[outer]]}\n{str_replace_all(.data[[inner]], ' ', '\n')}\nn = {n}",
#    explode_outer = "(In|De)creased", explode_r = 0.3,
#    outer_label = "text", outer_min_p = 0, outer_max_p = 0.02
#  )

## ----add-donut-customised, eval=TRUE, echo=FALSE, fig.cap = "*Split-Donut chart exploding key regions and customising labels.*"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-donut-customised-1.png")

## ----bwfl, eval = FALSE-------------------------------------------------------
#  bwfl <- here::here(
#    "data", "H3K27ac", glue("{levels(samples$treatment)}_cov_chr10.bw")
#  ) %>%
#    BigWigFileList() %>%
#    setNames(levels(samples$treatment))

## ----cytobands----------------------------------------------------------------
data("grch37.cytobands")
head(grch37.cytobands)

## ----gr, eval = FALSE---------------------------------------------------------
#  gr <- filter(tmm_results, hmp_fdr < 0.05, str_detect(bestOverlap, '^Prom'))[1]

## ----plot-hfgc, eval = FALSE--------------------------------------------------
#  plotHFGC(gr, cytobands = grch37.cytobands, coverage = bwfl)

## ----cov-list, eval = FALSE---------------------------------------------------
#  cov_list <- list(H3K27ac = bwfl)
#  cov_colour <- list(H3K27ac = treat_colours[levels(samples$treatment)])
#  plotHFGC(
#    gr, cytobands = grch37.cytobands,
#    coverage =  cov_list, linecol = cov_colour, zoom = 50, rotation.title = 90
#  )

## ----cov-annot, eval = FALSE--------------------------------------------------
#  cov_annot <- splitAsList(tmm_results, tmm_results$status) %>%
#    endoapply(granges)
#  cov_annot <- list(H3K27ac = cov_annot)

## ----gene-models, eval = FALSE------------------------------------------------
#  gene_models <- gencode$exon %>%
#    select(
#      type, gene = gene_id, exon = exon_id, transcript = transcript_id,
#      symbol = gene_name
#    )

## ----plot-hfgc-with-annot, eval = FALSE---------------------------------------
#  names(region_colours) <- names(regions)
#  plotHFGC(
#    gr, cytobands = grch37.cytobands,
#    features = regions, featcol = region_colours, featstack = "dense",
#    coverage =  cov_list, linecol = cov_colour,
#    genes = gene_models, genecol = "wheat", collapseTranscripts = FALSE,
#    zoom = 350, shift = -1e5,
#    rotation.title = 90, covsize = 10, genesize = 1, featsize = 5,
#    annotation = cov_annot, annotcol = status_colours[c("Unchanged", "Increased")]
#  )

## ----add-hfgc, eval=TRUE, echo=FALSE, fig.cap = "The addition of an annotation track for the coverage tracks shows which regions were retained during the analysis, as well as those which were considered as showing changed or unchanged signal"----
knitr::include_graphics("differential_signal_sliding_files/figure-gfm/plot-hfgc-with-annot-1.png")

## ----session-info-------------------------------------------------------------
sessionInfo()

