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plotting.R
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plotting.R
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#' HiContacts plotting functionalities
#'
#' @name HiContacts-plots
#'
#' @description
#' Several plots can be generated in HiContacts:
#' - Hi-C contact matrices
#' - Distance-dependant interaction frequency decay (a.k.a. "Distance law" or "P(s)")
#' - Virtual 4C profiles
#' - Scalograms
#' - Saddle plots
NULL
################################################################################
# #
# Matrix #
# #
################################################################################
#' Plotting a contact matrix
#'
#' @name plotMatrix
#' @aliases plotMatrix,HiCExperiment-method
#' @aliases plotMatrix,HiCExperiment-method
#' @aliases plotMatrix,GInteractions-method
#' @aliases plotMatrix,matrix-method
#'
#' @param x A HiCExperiment object
#' @param compare.to Compare to a second HiC matrix in the lower left corner
#' @param use.scores Which scores to use in the heatmap
#' @param scale Any of 'log10', 'log2', 'linear', 'exp0.2' (Default: 'log10')
#' @param limits color map limits
#' @param maxDistance maximum distance. If provided, the heatmap is plotted
#' horizontally
#' @param loops Loops to plot on top of the heatmap, provided as `GInteractions`
#' @param borders Borders to plot on top of the heatmap, provided as `GRanges`
#' @param tracks Named list of bigwig tracks imported as `Rle`
#' @param dpi DPI to create the plot (Default: 500)
#' @param rasterize Whether the generated heatmap is rasterized or vectorized
#' (Default: TRUE)
#' @param symmetrical Whether to enforce a symetrical heatmap (Default: TRUE)
#' @param chrom_lines Whether to display separating lines between chromosomes,
#' should any be necessary (Default: TRUE)
#' @param show_grid Whether to display an underlying grid (Default: FALSE)
#' @param caption Whether to display a caption (Default: TRUE)
#' @param cmap Color scale to use. (Default: bgrColors() if limits are c(-1, 1)
#' and coolerColors() otherwise)
#' @return ggplot object
#'
#' @importFrom ggrastr geom_tile_rast
#' @importFrom tibble as_tibble
#' @importFrom dplyr mutate
#' @importFrom dplyr select
#' @importFrom dplyr group_by
#' @importFrom dplyr summarize
#' @importFrom dplyr slice_max
#' @importFrom dplyr distinct
#' @importFrom dplyr left_join
#' @importFrom dplyr rename
#' @importFrom dplyr left_join
#' @importFrom dplyr rename
#' @importFrom GenomicRanges seqnames
#' @importFrom scales oob_squish
#' @importFrom scales unit_format
#' @examples
#' contacts_yeast <- contacts_yeast()
#' plotMatrix(
#' contacts_yeast,
#' use.scores = 'balanced',
#' scale = 'log10',
#' limits = c(-4, -1)
#' )
NULL
#' @rdname plotMatrix
#' @export
setMethod("plotMatrix", "HiCExperiment", function(
x,
compare.to = NULL,
use.scores = 'balanced',
scale = 'log10',
maxDistance = NULL,
loops = NULL,
borders = NULL,
tracks = NULL,
limits = NULL,
dpi = 500,
rasterize = TRUE,
symmetrical = TRUE,
chrom_lines = TRUE,
show_grid = FALSE,
cmap = NULL,
caption = TRUE
) {
if (!use.scores %in% names(scores(x)))
stop(paste0("Queried scores not found."))
if (!is.null(compare.to)) {
gis_x <- interactions(x)
gis_y <- interactions(compare.to)
gis <- c(
as(gis_y, 'GInteractions'),
InteractionSet::swapAnchors(
as(gis_x, 'GInteractions'),
mode = 'reverse'
)
)
gis <- gis[pairdist(gis) != 0]
p <- plotMatrix(
gis,
use.scores = use.scores,
scale = scale,
maxDistance = maxDistance,
loops = loops,
borders = borders,
tracks = tracks,
limits = limits,
dpi = dpi,
rasterize = rasterize,
symmetrical = FALSE,
chrom_lines = chrom_lines,
show_grid = show_grid,
cmap = cmap
)
}
else {
p <- plotMatrix(
interactions(x),
use.scores = use.scores,
scale = scale,
maxDistance = maxDistance,
loops = loops,
borders = borders,
tracks = tracks,
limits = limits,
dpi = dpi,
rasterize = rasterize,
symmetrical = symmetrical,
chrom_lines = chrom_lines,
show_grid = show_grid,
cmap = cmap
)
}
if (caption & is.null(tracks)) {
f <- fileName(x)
if (!is.null(compare.to)) {
f <- paste0(fileName(x), ' | ', fileName(compare.to))
print(f)
}
p <- p + ggplot2::labs(
caption = paste(
sep = '\n',
paste0('file: ', f),
paste0('resolution: ', resolution(x)),
paste0('focus: ', focus(x)),
paste0('scores: ', use.scores),
paste0('scale: ', scale)
)
)
}
return(p)
})
#' @rdname plotMatrix
#' @export
setMethod("plotMatrix", "GInteractions", function(
x,
use.scores = NULL,
scale = 'log10',
maxDistance = NULL,
loops = NULL,
borders = NULL,
tracks = NULL,
limits = NULL,
dpi = 500,
rasterize = TRUE,
symmetrical = TRUE,
chrom_lines = TRUE,
show_grid = FALSE,
cmap = NULL
) {
`%over%` <- IRanges::`%over%`
## -- Extract scores
gis <- x
if (!is.null(use.scores)) {
gis$score <- S4Vectors::mcols(gis)[, use.scores]
}
else {
if ("balanced" %in% colnames(S4Vectors::mcols(gis))) {
gis$score <- S4Vectors::mcols(gis)[, 'balanced']
}
else {
gis$score <- S4Vectors::mcols(gis)[, 'count']
}
}
## -- Put metric to plot in `score` column
has_negative_scores <- any(gis$score < 0, na.rm = TRUE)
## -- Scale score
if (scale == 'log10') {
gis$score <- log10(gis$score)
}
else if (scale == 'log2') {
gis$score <- log2(gis$score)
}
else if (scale == 'exp0.2') {
gis$score <- gis$score^0.2
}
else if (scale == 'linear') {
gis$score <- gis$score
}
## -- Set matrix limits
if (!is.null(limits)) {
m <- limits[1]
M <- limits[2]
}
else {
.scores <- gis$score[pairdist(gis) != 0 & !is.na(pairdist(gis) != 0)]
.scores <- .scores[!is.na(.scores)]
.scores <- .scores[!is.infinite(.scores)]
M <- max(.scores)
m <- min(.scores)
limits <- c(m, M)
}
## -- Choose color map
if (is.null(cmap)) {
if (has_negative_scores | {m == -1 & M == 1}) {
cmap <- bgrColors()
}
else {
cmap <- coolerColors()
}
}
# -- Define plotting approach
if (rasterize) {
plotFun <- ggrastr::geom_tile_rast(
raster.dpi = dpi,
width = GenomicRanges::width(gis)[[1]][1],
height = GenomicRanges::width(gis)[[1]][1]
)
}
else {
plotFun <- ggplot2::geom_tile()
}
## -- If loops are provided, filter them and add
if (!is.null(loops) & is.null(maxDistance)) {
filtered_loops <- tibble::as_tibble(
loops[anchors(loops)[['first']] %over% gis & anchors(loops)[['second']] %over% gis]
)
p_loops <- ggplot2::geom_point(
data = filtered_loops,
mapping = ggplot2::aes(
x = start1+(end1-start1)/2, y = start2+(end2-start2)/2
),
inherit.aes = FALSE,
shape = 21,
fill = "#76eaff00",
color = "#000000",
size = 3
)
}
else {
p_loops <- NULL
}
## -- If borders are provided, filter them and add
if (!is.null(borders) & is.null(maxDistance)) {
filtered_borders <- tibble::as_tibble(
borders[borders %over% gis]
)
p_borders <- ggplot2::geom_point(
data = filtered_borders,
mapping = ggplot2::aes(x = start+(end-start)/2, y = start+(end-start)/2),
inherit.aes = FALSE,
shape = 23,
fill = "#76eaff",
color = "#000000",
size = 1
)
}
else {
p_borders <- NULL
}
## -- If tracks are provided, filter them and add
if (!is.null(tracks) & is.null(maxDistance)) {
re <- reduce(regions(gis))
# if (length(re) > 1) {
# warning("Input track cannot be aligned to interactions with non-contiguous regions.")
# p_tracks <- ggplot2::ggplot() + ggplot2::theme_void()
# }
# else {
ntracks <- length(tracks)
step <- GenomicRanges::width(InteractionSet::anchors(gis[1])[[1]])/5
filtered_tracks <- purrr::imap(tracks, ~ .x[re] |>
tibble::as_tibble() |>
dplyr::group_by(group=(0:(n()-1))%/%{step}) |>
dplyr::summarize(value = mean(value, na.rm = TRUE)) |>
dplyr::mutate(
# pos = seq(IRanges::start(re), IRanges::end(re), by = step) + step/2,
track = .y
)
) |> dplyr::bind_rows(.id = 'track')
p_tracks <- ggplot2::ggplot() +
ggplot2::geom_area(
data = filtered_tracks,
mapping = ggplot2::aes(x = group, y = value),
fill = "#393939",
color = NA,
) +
ggplot2::scale_x_continuous(expand = c(0, 0)) +
ggplot2::scale_y_reverse(expand = c(0, 0)) +
ggplot2::facet_grid(track~., scales = 'free') +
ggplot2::theme_void() +
ggplot2::theme(
strip.background = ggplot2::element_blank(),
strip.text = ggplot2::element_blank()
)
# }
}
else {
p_tracks <- ggplot2::ggplot() + ggplot2::theme_void()
}
# -- Check number of chromosomes that were extracted
nseqnames <- length(unique(as.vector(
GenomicRanges::seqnames(InteractionSet::regions(gis))
)))
if (nseqnames == 1 | {nseqnames == 2 & all(
GenomicRanges::seqnames(InteractionSet::anchors(gis, "first")) !=
GenomicRanges::seqnames(InteractionSet::anchors(gis, "second"))
)}) { ## SINGLE CHROMOSOME MAP or 2 CHROMOSOMES TRANS INTERSECTION
if (is.null(maxDistance)) { ##### REGULAR SQUARE MATRIX
## -- Convert gis to table and extract x/y
mat <- gis |>
tibble::as_tibble() |>
dplyr::mutate(
x = floor(end1 - (end1 - start1) / 2),
y = floor(end2 - (end2 - start2) / 2)
) |>
tidyr::drop_na(score)
## -- Clamp scores to limits
mat$score = scales::oob_squish(mat$score, c(m, M))
## -- Add lower triangular matrix scores (if symetrical)
if (symmetrical) {
mat <- rbind(
mat,
mat |>
dplyr::mutate(x2 = y, y = x, x = x2) |>
dplyr::select(-x2)
)
bb <- InteractionSet::boundingBox(x)
bbOne <- InteractionSet::anchors(bb)[[1]]
bbTwo <- InteractionSet::anchors(bb)[[2]]
fraction_ov <- GenomicRanges::width(GenomicRanges::pintersect(bbOne, bbTwo)) /
{{GenomicRanges::width(bbOne) + GenomicRanges::width(bbTwo)}/2}
if (fraction_ov > 0.9) {
coords <- c(bbOne, bbTwo)
mat <- mat |>
dplyr::filter(x >= GenomicRanges::start(coords[2]) &
x <= GenomicRanges::end(coords[2])) |>
dplyr::filter(y >= GenomicRanges::start(coords[1]) &
y <= GenomicRanges::end(coords[1]))
}
}
## -- Plot matrix
p <- ggMatrix(mat, cols = cmap, limits = limits, grid = show_grid) +
plotFun +
p_loops +
p_borders +
ggplot2::labs(
x = unique(mat$seqnames1),
y = "Genome coordinates"
) +
ggplot2::scale_x_continuous(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6), position = 'top') +
ggplot2::scale_y_reverse(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6))
## -- Add tracks
if (length(p_tracks$layers)) {
p <- p +
ggplot2::theme(
plot.margin = grid::unit(c(5.5, 5.5, 5.5 + 10*ntracks, 5.5), "points")
) + patchwork::inset_element(
p_tracks,
left = 0,
bottom = -0.1*ntracks,
right = 1,
top = 0,
align_to = 'panel',
clip = FALSE
)
}
}
else { ##### HORIZONTAL TRIANGULAR MATRIX
## -- Convert gis to table and extract x/y
df <- gis |>
tibble::as_tibble() |>
dplyr::mutate(
diag = center2 - center1,
x = center1 + (center2 - center1)/2
) |>
tidyr::drop_na(score) |>
dplyr::filter(diag <= maxDistance)
## -- Clamp scores to limits
df <- dplyr::mutate(df, score = scales::oob_squish(score, c(m, M)))
## -- Plot matrix
p <- ggHorizontalMatrix(df, cols = cmap, limits = limits) +
plotFun +
p_loops +
p_borders +
ggplot2::labs(
x = "Genomic location",
y = "Distance"
)
}
}
else { ##### MULTI-CHROMOSOMES MAP
## -- Abort if more than 1 chr. is plotted AND maxDistance is provided
if (!is.null(maxDistance)) stop(
"Horizontal, multi-chromosomes maps are not supported."
)
chroms <- gis |>
tidyr::as_tibble() |>
dplyr::group_by(seqnames2) |>
dplyr::slice_max(order_by = end2, n = 1) |>
dplyr::select(seqnames2, end2) |>
dplyr::distinct()
chroms$cumlength <- cumsum(c(0, chroms$end2)[seq_len(nrow(chroms))])
chroms$end2 <- NULL
mat <- gis |>
tibble::as_tibble() |>
dplyr::left_join(chroms, by = c(seqnames1 = "seqnames2")) |>
dplyr::rename(cumlength_x = cumlength) |>
dplyr::left_join(chroms, by = c(seqnames2 = "seqnames2")) |>
dplyr::rename(cumlength_y = cumlength) |>
dplyr::mutate(
x = floor(end1 - (end1 - start1) / 2) + cumlength_x,
y = floor(end2 - (end2 - start2) / 2) + cumlength_y
)
mat <- dplyr::mutate(
mat,
score = ifelse(score > M, M, ifelse(score < m, m, score))
)
mat <- rbind(
mat,
mat |>
dplyr::mutate(x2 = y, y = x, x = x2) |>
dplyr::select(-x2)
)
## -- Plot matrix
p <- ggMatrix(mat, cols = cmap, limits = limits, grid = show_grid) +
plotFun +
ggplot2::labs(
x = "Genome coordinates",
y = "Genome coordinates"
) +
ggplot2::scale_x_continuous(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6), position = 'top') +
ggplot2::scale_y_reverse(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6))
if (chrom_lines) {
p <- p +
ggplot2::geom_hline(
yintercept = chroms$cumlength[-1],
colour = "black", alpha = 0.75, linewidth = 0.15
) +
ggplot2::geom_vline(
xintercept = chroms$cumlength[-1],
colour = "black", alpha = 0.75, linewidth = 0.15
)
}
## -- Add tracks
if (length(p_tracks$layers)) {
p <- p +
ggplot2::theme(
plot.margin = grid::unit(c(5.5, 5.5, 5.5 + 10*ntracks, 5.5), "points")
) + patchwork::inset_element(
p_tracks,
left = 0,
bottom = -0.1*ntracks,
right = 1,
top = 0,
align_to = 'panel',
clip = FALSE
)
}
}
p
})
#' @rdname plotMatrix
#' @export
setMethod("plotMatrix", "matrix", function(
x,
scale = 'log10',
limits = NULL,
dpi = 500,
rasterize = TRUE,
cmap = NULL
) {
mat <- x
has_negative_scores <- any(mat < 0, na.rm = TRUE)
## -- Scale score
if (scale == 'log10') {
mat <- log10(mat)
}
else if (scale == 'log2') {
mat <- log2(mat)
}
else if (scale == 'exp0.2') {
mat <- mat^0.2
}
else if (scale == 'linear') {
mat <- mat
}
## -- Set matrix limits
if (!is.null(limits)) {
m <- limits[1]
M <- limits[2]
}
else {
mat0 <- mat
sdiag(mat0, 0) <- NA
.scores <- mat0
.scores <- .scores[!is.na(.scores)]
.scores <- .scores[!is.infinite(.scores)]
M <- max(.scores)
m <- min(.scores)
limits <- c(m, M)
}
## -- Choose color map
if (is.null(cmap)) {
if (has_negative_scores) {
cmap <- bgrColors()
}
else {
cmap <- coolerColors()
}
}
# -- Define plotting approach
if (rasterize) {
plotFun <- ggrastr::geom_tile_rast(
raster.dpi = dpi
)
}
else {
plotFun <- ggplot2::geom_tile()
}
## -- Convert gis to table and extract x/y
colnames(mat) <- paste0("bin", seq_len(ncol(mat)))
mat <- tibble::as_tibble(mat) |>
dplyr::mutate(x = seq_len(ncol(mat))) |>
tidyr::pivot_longer(-x, names_to = 'y', values_to = 'score') |>
dplyr::mutate(y = gsub('bin', '', y) |> as.numeric()) |>
tidyr::drop_na(score)
## -- Clamp scores to limits
mat <- dplyr::mutate(mat, score = scales::oob_squish(score, c(m, M)))
## -- Plot matrix
p <- ggMatrix(mat, cols = cmap, limits = limits) +
plotFun +
ggplot2::labs(
x = "Bin number",
y = "Bin number"
) +
ggplot2::scale_x_continuous(expand = c(0, 0), position = 'top') +
ggplot2::scale_y_reverse(expand = c(0, 0))
p
})
#' @rdname plotMatrix
#' @export
setMethod("plotMatrix", "AggrHiCExperiment", function(
x,
use.scores = 'balanced',
scale = 'log10',
maxDistance = NULL,
loops = NULL,
borders = NULL,
limits = NULL,
dpi = 500,
rasterize = TRUE,
chrom_lines = TRUE,
show_grid = FALSE,
cmap = NULL,
caption = TRUE
) {
pairs <- topologicalFeatures(x, 'targets')
is1D <- all(S4Vectors::first(pairs) == S4Vectors::second(pairs))
p <- plotMatrix(
interactions(x),
use.scores = use.scores,
scale = scale,
maxDistance = maxDistance,
loops = loops,
borders = borders,
limits = limits,
dpi = dpi,
rasterize = rasterize,
symmetrical = ifelse(is1D, TRUE, FALSE),
chrom_lines = chrom_lines,
show_grid = show_grid,
cmap = cmap
)
if (caption) {
p <- p + ggplot2::labs(
caption = paste(
sep = '\n',
paste0('file: ', fileName(x)),
paste0('resolution: ', resolution(x)),
paste0('focus: ', focus(x)),
paste0('scores: ', use.scores),
paste0('scale: ', scale)
)
)
}
return(p)
})
#' @rdname plotMatrix
#' @export
setMethod("montage", "AggrHiCExperiment", function(
x,
use.scores = 'balanced',
scale = 'log10',
limits = NULL,
dpi = 500,
rasterize = TRUE,
cmap = NULL
) {
pairs <- topologicalFeatures(x, 'targets')
is1D <- ifelse(is(pairs, 'GRanges'), TRUE, FALSE)
sli <- slices(x, use.scores)
snips <- lapply(seq_len(dim(sli)[3]), function(K){
mat <- sli[, , K]
if (is1D) mat[lower.tri(mat)] <- t(mat)[lower.tri(mat)]
plotMatrix(
mat,
scale = scale,
limits = limits,
dpi = dpi,
rasterize = rasterize,
cmap = cmap
) +
ggplot2::theme_void() +
ggplot2::theme(legend.position = "none") +
ggplot2::theme(plot.margin = margin(.002, .002, .002, .002, "npc"))
})
p <- patchwork::wrap_plots(snips)
})
ggMatrix <- function(mat, ticks = TRUE, grid = FALSE, cols = coolerColors(), limits) {
p <- ggplot2::ggplot(mat, ggplot2::aes(x, y, fill = score))
p <- p + ggplot2::scale_fill_gradientn(
colors = cols,
na.value = "#FFFFFF",
limits = limits
) +
ggplot2::guides(fill = ggplot2::guide_colorbar(barheight = ggplot2::unit(.1, "npc"), barwidth = 0.5, frame.colour = "black")) +
ggplot2::coord_fixed() +
ggthemeHiContacts(ticks, grid)
p
}
ggHorizontalMatrix <- function(df, cols = coolerColors(), limits) {
p <- ggplot2::ggplot(df, ggplot2::aes(x, diag, fill = score))
r <- 1/sqrt(2)/sqrt(2)
p <- p + ggplot2::scale_fill_gradientn(
colors = cols,
na.value = "#FFFFFF",
limits = limits
) +
ggplot2::scale_x_continuous(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6)) +
ggplot2::scale_y_continuous(expand = c(0, 0), labels = scales::unit_format(unit = "M", scale = 1e-6)) +
ggplot2::guides(fill = ggplot2::guide_colorbar(barheight = ggplot2::unit(.1, "npc"), barwidth = 0.5, frame.colour = "black")) +
ggplot2::coord_fixed(ratio = r) +
ggthemeHiContacts(ticks = TRUE, grid = FALSE)
p
}
################################################################################
# #
# P(s) #
# #
################################################################################
#' Plotting a P(s) distance law
#'
#' @name plotPs
#' @aliases plotPs
#' @aliases plotPsSlope
#'
#' @param x the output data.frame of `distanceLaw` function
#' @param mapping aes to pass on to ggplot2
#' @param xlim xlim
#' @param ylim ylim
#' @return ggplot
#'
#' @importFrom scales trans_breaks
#' @importFrom scales trans_format
#' @examples
#' ## Single P(s)
#'
#' contacts_yeast <- contacts_yeast()
#' ps <- distanceLaw(contacts_yeast)
#' plotPs(ps, ggplot2::aes(x = binned_distance, y = norm_p))
#'
#' ## Comparing several P(s)
#'
#' contacts_yeast <- contacts_yeast()
#' contacts_yeast_eco1 <- contacts_yeast_eco1()
#' ps_wt <- distanceLaw(contacts_yeast)
#' ps_wt$sample <- 'WT'
#' ps_eco1 <- distanceLaw(contacts_yeast_eco1)
#' ps_eco1$sample <- 'eco1'
#' ps <- rbind(ps_wt, ps_eco1)
#' plotPs(ps, ggplot2::aes(x = binned_distance, y = norm_p, group = sample, color = sample))
#' plotPsSlope(ps, ggplot2::aes(x = binned_distance, y = slope, group = sample))
NULL
#' @rdname plotPs
#' @export
plotPs <- function(x, mapping, xlim = c(5000, 4.99e5), ylim = c(1e-8, 1e-4)) {
p <- ggplot2::ggplot(x, mapping = mapping) +
ggplot2::geom_line() +
ggplot2::theme_minimal() +
ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank()) +
ggplot2::annotation_logticks() +
ggplot2::labs(x = "Genomic distance", y = "Contact frequency")
if (!is.null(ylim)) {
p <- p + ggplot2::scale_y_log10(
limits = ylim,
expand = c(0, 0),
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
)
}
else {
p <- p + ggplot2::scale_y_log10(
expand = c(0, 0),
breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = scales::trans_format("log10", scales::math_format(10^.x))
)
}
if (!is.null(xlim)) {
p <- p + ggplot2::scale_x_log10(
limits = xlim,
expand = c(0, 0),
breaks = c(1, 10, 100, 1000, 10000, 1e+05, 1e+06, 1e+07, 1e+08, 1e+09, 1e+10),
labels = c('1', '10', '100', '1kb', '10kb', '100kb', '1Mb', '10Mb', '100Mb', '1Gb', '10Gb')
)
}
else {
p <- p + ggplot2::scale_x_log10(
expand = c(0, 0),
breaks = c(1, 10, 100, 1000, 10000, 1e+05, 1e+06, 1e+07, 1e+08, 1e+09, 1e+10),
labels = c('1', '10', '100', '1kb', '10kb', '100kb', '1Mb', '10Mb', '100Mb', '1Gb', '10Gb')
)
}
p
}
#' @rdname plotPs
#' @export
plotPsSlope <- function(x, mapping, xlim = c(5000, 4.99e5), ylim = c(-3, 0)) {
gg <- ggplot2::ggplot(x, mapping = mapping) +
ggplot2::geom_line() +
ggplot2::theme_minimal() +
ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank()) +
ggplot2::scale_y_continuous(
limits = ylim,
expand = c(0, 0)
) +
ggplot2::scale_x_log10(
limits = xlim,
expand = c(0, 0),
breaks = c(1, 10, 100, 1000, 10000, 1e+05, 1e+06, 1e+07, 1e+08, 1e+09, 1e+10),
labels = c('1', '10', '100', '1kb', '10kb', '100kb', '1Mb', '10Mb', '100Mb', '1Gb', '10Gb')
) +
ggplot2::annotation_logticks(sides = "b") +
ggplot2::labs(x = "Genomic distance", y = "Slope of P(s)")
gg
}
################################################################################
# #
# virtual 4C #
# #
################################################################################
#' Plotting virtual 4C profiles
#'
#' @name plot4C
#' @aliases plot4C
#'
#' @param x GRanges, generally the output of `virtual4C()`
#' @param mapping aes to pass on to ggplot2 (default:
#' `ggplot2::aes(x = center, y = score, col = seqnames)`)
#' @return ggplot
#'
#' @import tibble
#' @importFrom scales unit_format
#' @examples
#' contacts_yeast <- contacts_yeast()
#' v4C <- virtual4C(contacts_yeast, GenomicRanges::GRanges('II:490001-510000'))
#' plot4C(v4C)
NULL
#' @rdname plot4C
#' @export
plot4C <- function(x, mapping = ggplot2::aes(x = center, y = score, col = seqnames)) {
x <- tibble::as_tibble(x)
p <- ggplot2::ggplot(x, mapping = mapping) +
# ggplot2::geom_line() +
ggplot2::geom_area(position = "identity", alpha = 0.2) +
ggplot2::theme_minimal() +
ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank()) +
ggplot2::labs(x = "Position", y = "Contacts with viewpoint") +
ggplot2::scale_x_continuous(
expand = c(0, 0),
labels = scales::unit_format(unit = "M", scale = 1e-6)
)
p
}
################################################################################
# #
# Scalograms #
# #
################################################################################
#' Plotting scalograms
#'
#' @name plotScalogram
#' @aliases plotScalogram
#'
#' @param x GRanges, the output of `scalogram()`
#' @param ylim Range of distances to use for y-axis in scalograms
#' @return ggplot
#'
#' @import tibble
#' @examples
#' contacts_yeast <- HiCExperiment::contacts_yeast()
#' pairsFile(contacts_yeast) <- HiContactsData::HiContactsData(
#' 'yeast_wt', format = 'pairs.gz'
#' )
#' scalo <- scalogram(contacts_yeast['II'])
#' plotScalogram(scalo)
NULL
#' @rdname plotScalogram
#' @export
plotScalogram <- function(x, ylim = c(5e2, 1e5)) {
x_ <- dplyr::mutate(x, dist_quantile = scales::oob_squish(dist_quantile, c(ylim[[1]], ylim[[2]]))) |>
dplyr::mutate(y0 = ylim[[1]])
p <- ggplot2::ggplot() +
ggplot2::theme_minimal() +
ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank()) +
ggplot2::scale_x_continuous(expand = c(0, 0)) +
ggplot2::scale_y_log10(
expand = c(0, 0),
limits = c(ylim[[1]], ylim[[2]])
) +
ggplot2::annotation_logticks(sides = 'l') +
ggplot2::labs(x = 'Position along chr.', y = 'Distance of interactions')
if (length(unique(x_$prob)) == 3) {
x_$prob <- as.numeric(as.character(x_$prob))
low <- sort(unique(x_$prob))[1]
mid <- sort(unique(x_$prob))[2]
high <- sort(unique(x_$prob))[3]
x_$prob <- dplyr::case_when(
x_$prob == low ~ 'low',
x_$prob == mid ~ 'mid',
x_$prob == high ~ 'high'
)
diff <- {high - low} * 100
p <- p +
ggplot2::geom_ribbon(
data = tidyr::pivot_wider(x_[x_$prob != 'mid',], names_from = prob, values_from = dist_quantile),
mapping = ggplot2::aes(x = binned_pos, ymin = low, ymax = high, fill = y0),
col = 'black', linewidth = 0.2, linetype = 'dashed'
) +
ggplot2::geom_line(
data = x_[x_$prob == 'mid',],
ggplot2::aes(x = binned_pos, y = dist_quantile, col = prob)
) +
ggplot2::scale_fill_gradient(low = '#b5b5b54d', high = '#b5b5b54d') +
ggplot2::scale_color_manual(values = '#000000') +
ggplot2::labs(fill = paste0(diff, '% (', low, '% - ', high, '%)\nof interactions\nwithin this range')) +
ggplot2::labs(col = paste0(mid*100, '% of interaction distance')) +
ggplot2::theme(legend.text = ggplot2::element_blank(), legend.position = 'bottom')
}
else {
p <- p +
ggplot2::geom_ribbon(data = x_, ggplot2::aes(
x = binned_pos, ymax = dist_quantile,
group = prob, fill = prob
)) +
ggplot2::aes(ymin = y0) +
ggplot2::scale_fill_brewer(palette = 'Spectral')
}
if (length(unique(x_$chr)) > 1) {
p <- p + ggplot2::facet_wrap(~chr)
}
p
}
################################################################################
# #
# Saddle plots #
# #
################################################################################
#' Plotting saddle plots
#'
#' @name plotSaddle
#' @aliases plotSaddle
#'
#' @param x a HiCExperiment object with a stored `eigens` metadata
#' @param nbins Number of bins to use to discretize the eigenvectors
#' @param limits limits for color map being used
#' @param plotBins Whether to plot the distribution of bins on top of the plot
#' @param BPPARAM a BiocParallel registered method
#' @return ggplot
NULL
#' @rdname plotSaddle
#' @importFrom GenomicRanges resize
#' @export
plotSaddle <- function(x, nbins = 50, limits = c(-1, 1), plotBins = FALSE, BPPARAM = BiocParallel::bpparam()) {
if (is.null(metadata(x)$eigens)) stop(
"No eigen vector found in metadata. Run getCompartments(x) first."
)
eigens <- metadata(x)$eigens
## -- Filter and bin regions by their eigenvector score
eigens$eigen_bin <- cut(
eigens$eigen, breaks = stats::quantile(
eigens$eigen[eigens$eigen != 0],
probs = seq(0.025, 0.975, length.out = nbins+1)
),
include.lowest = TRUE
) |> as.numeric()
## -- Compute over-expected score in each pair of eigen bins
if (interactive())
BiocParallel::bpprogressbar(BPPARAM) <- TRUE
df <- BiocParallel::bplapply(
BPPARAM = BPPARAM,
as.character(unique(seqnames(eigens))),
function(chr) {.saddle(x[chr], eigens)}
) |> dplyr::bind_rows()
dat <- df |>
dplyr::group_by(eigen_bin1, eigen_bin2) |>
dplyr::summarize(score = mean(detrended), .groups = 'drop') |>
dplyr::mutate(
x = eigen_bin1 / nbins,
y = eigen_bin2 / nbins,