R/do_LigandReceptorPlot.R
In enblacar/SCpubr: Generate Publication Ready Visualizations of Single Cell Transcriptomics Data
Defines functions
do_LigandReceptorPlot
Documented in
do_LigandReceptorPlot
#' Visualize Ligand-Receptor analysis output.
#'
#' This function makes use of [liana](https://github.com/saezlab/liana) package to run Ligand-Receptor analysis. Takes the output of liana and generates a dot-plot visualization according to the user's specifications.
#'
#' @inheritParams doc_function
#' @param liana_output \strong{\code{\link[tibble]{tibble}}} | Object resulting from running \link[liana]{liana_wrap} and \link[liana]{liana_aggregate}.
#' @param split.by \strong{\code{\link[base]{character}}} | Whether to further facet the plot on the y axis by common ligand.complex or receptor.complex. Values to provide: NULL, ligand.complex, receptor.complex.
#' @param keep_source,keep_target \strong{\code{\link[base]{character}}} | Identities to keep for the source/target of the interactions. NULL otherwise.
#' @param top_interactions \strong{\code{\link[base]{numeric}}} | Number of unique interactions to retrieve ordered by magnitude and specificity. It does not necessarily mean that the output will contain as many, but rather an approximate value.
#' @param top_interactions_by_group \strong{\code{\link[base]{logical}}} | Enforce the value on \strong{\code{top_interactions}} to be applied to each group in \strong{\code{source}} column.
#' @param dot_border \strong{\code{\link[base]{logical}}} | Whether to draw a black border in the dots.
#' @param dot.size \strong{\code{\link[base]{numeric}}} | Size aesthetic for the dots.
#' @param sort.by \strong{\code{\link[base]{character}}} | How to arrange the top interactions. Interactions are sorted and then the top N are retrieved and displayed. This takes place after subsetting for \strong{\code{keep_source}} and \strong{\code{keep_target}} One of:
#' \itemize{
#' \item \emph{\code{A}}: Sorts by specificity.
#' \item \emph{\code{B}}: Sorts by magnitude.
#' \item \emph{\code{C}}: Sorts by specificity, then magnitude (gives extra weight to specificity).
#' \item \emph{\code{D}}: Sorts by magnitude, then specificity (gives extra weight to magnitude). Might lead to the display of non-significant results.
#' \item \emph{\code{E}}: Sorts by specificity and magnitude equally.
#' }
#' @param specificity,magnitude \strong{\code{\link[base]{character}}} | Which columns to use for \strong{\code{specificity}} and \strong{\code{magnitude}}.
#' @param invert_specificity,invert_magnitude \strong{\code{\link[base]{logical}}} | Whether to \strong{\code{-log10}} transform \strong{\code{specificity}} and \strong{\code{magnitude}} columns.
#' @param sorting.type.specificity,sorting.type.magnitude \strong{\code{\link[base]{character}}} | Whether the sorting of e \strong{\code{magnitude}} or \strong{\code{specificity}} columns is done in ascending or descending order. This synergises with the value of e \strong{\code{invert_specificity}} and e \strong{\code{invert_magnitude}} parameters.
#' @param compute_ChordDiagrams \strong{\code{\link[base]{logical}}} | Whether to also compute Chord Diagrams for both the number of interactions between source and target but also between ligand.complex and receptor.complex.
#' @param sort_interactions_alphabetically \strong{\code{\link[base]{logical}}} | Sort the interactions to be plotted alphabetically (\strong{\code{TRUE}}) or keep them in their original order in the matrix (\strong{\code{FALSE}}).
#' @param return_interactions \strong{\code{\link[base]{logical}}} | Whether to return the data.frames with the interactions so that they can be plotted as chord plots using other package functions.
#'
#' @return A ggplot2 plot with the results of the Ligand-Receptor analysis.
#' @export
#'
#' @example /man/examples/examples_do_LigandReceptorPlot.R
do_LigandReceptorPlot <- function(liana_output,
split.by = NULL,
keep_source = NULL,
keep_target = NULL,
top_interactions = 25,
top_interactions_by_group = FALSE,
dot_border = TRUE,
magnitude = "sca.LRscore",
specificity = "aggregate_rank",
sort.by = "E",
sorting.type.specificity = "descending",
sorting.type.magnitude = "descending",
border.color = "black",
axis.text.x.angle = 45,
legend.position = "bottom",
legend.type = "colorbar",
legend.length = 20,
legend.width = 1,
legend.framecolor = "grey50",
legend.tickcolor = "white",
legend.framewidth = 0.5,
legend.tickwidth = 0.5,
use_viridis = FALSE,
viridis.palette = "G",
viridis.direction = 1,
sequential.palette = "YlGnBu",
sequential.direction = 1,
font.size = 14,
dot.size = 1,
font.type = "sans",
plot.grid = TRUE,
grid.color = "grey90",
grid.type = "dotted",
compute_ChordDiagrams = FALSE,
sort_interactions_alphabetically = FALSE,
number.breaks = 5,
plot.title.face = "bold",
plot.subtitle.face = "plain",
plot.caption.face = "italic",
axis.title.face = "bold",
axis.text.face = "plain",
legend.title.face = "bold",
legend.text.face = "plain",
return_interactions = FALSE,
invert_specificity = TRUE,
invert_magnitude = FALSE,
verbose = TRUE){
# Add lengthy error messages.
withr::local_options(.new = list("warning.length" = 8170))
# Checks for packages.
check_suggests(function_name = "do_LigandReceptorPlot")
`%>%` <- magrittr::`%>%`
`:=` <- rlang::`:=`
# Check logical parameters.
logical_list <- list("dot_border" = dot_border,
"plot.grid" = plot.grid,
"sort_interactions_alphabetically" = sort_interactions_alphabetically,
"use_viridis" = use_viridis,
"return_interactions" = return_interactions,
"invert_specificity" = invert_specificity,
"invert_magnitude" = invert_magnitude,
"verbose" = verbose,
"top_interactions_by_group" = top_interactions_by_group)
check_type(parameters = logical_list, required_type = "logical", test_function = is.logical)
# Check numeric parameters.
numeric_list <- list("font.size" = font.size,
"top_interactions" = top_interactions,
"legend.length" = legend.length,
"legend.width" = legend.width,
"legend.framewidth" = legend.framewidth,
"legend.tickwidth" = legend.tickwidth,
"dot.size" = dot.size,
"axis.text.x.angle" = axis.text.x.angle,
"viridis.direction" = viridis.direction,
"number.breaks" = number.breaks,
"sequential.direction" = sequential.direction)
check_type(parameters = numeric_list, required_type = "numeric", test_function = is.numeric)
# Check character parameters.
character_list <- list("split.by" = split.by,
"keep_source" = keep_source,
"keep_target" = keep_target,
"border.color" = border.color,
"legend.position" = legend.position,
"legend.type" = legend.type,
"legend.framecolor" = legend.framecolor,
"viridis.palette" = viridis.palette,
"legend.tickcolor" = legend.tickcolor,
"font.type" = font.type,
"grid.color" = grid.color,
"grid.type" = grid.type,
"sequential.palette" = sequential.palette,
"plot.title.face" = plot.title.face,
"plot.subtitle.face" = plot.subtitle.face,
"plot.caption.face" = plot.caption.face,
"axis.title.face" = axis.title.face,
"axis.text.face" = axis.text.face,
"legend.title.face" = legend.title.face,
"legend.text.face" = legend.text.face,
"sort.by" = sort.by,
"sorting.type.specificity" = sorting.type.specificity,
"sorting.type.magnitude" = sorting.type.magnitude)
check_type(parameters = character_list, required_type = "character", test_function = is.character)
# Check border color.
check_colors(border.color, parameter_name = "border.color")
# Check the colors provided to legend.framecolor and legend.tickcolor.
check_colors(legend.framecolor, parameter_name = "legend.framecolor")
check_colors(legend.tickcolor, parameter_name = "legend.tickcolor")
check_colors(grid.color, parameter_name = "grid.color")
check_parameters(parameter = font.type, parameter_name = "font.type")
check_parameters(parameter = legend.type, parameter_name = "legend.type")
check_parameters(parameter = legend.position, parameter_name = "legend.position")
check_parameters(parameter = viridis.palette, parameter_name = "viridis.palette")
check_parameters(parameter = grid.type, parameter_name = "grid.type")
check_parameters(parameter = axis.text.x.angle, parameter_name = "axis.text.x.angle")
check_parameters(parameter = number.breaks, parameter_name = "number.breaks")
check_parameters(plot.title.face, parameter_name = "plot.title.face")
check_parameters(plot.subtitle.face, parameter_name = "plot.subtitle.face")
check_parameters(plot.caption.face, parameter_name = "plot.caption.face")
check_parameters(axis.title.face, parameter_name = "axis.title.face")
check_parameters(axis.text.face, parameter_name = "axis.text.face")
check_parameters(legend.title.face, parameter_name = "legend.title.face")
check_parameters(legend.text.face, parameter_name = "legend.text.face")
check_parameters(viridis.direction, parameter_name = "viridis.direction")
check_parameters(sequential.direction, parameter_name = "sequential.direction")
colors.gradient <- compute_continuous_palette(name = ifelse(isTRUE(use_viridis), viridis.palette, sequential.palette),
use_viridis = use_viridis,
direction = ifelse(isTRUE(use_viridis), viridis.direction, sequential.direction),
enforce_symmetry = FALSE)
if (!is.null(split.by)){
assertthat::assert_that(split.by %in% c("receptor.complex", "ligand.complex"),
msg = paste0(add_cross,
crayon_body("Please select one of the following for "),
crayon_key("split.by"),
crayon_body(": "),
crayon_key("ligand.complex"),
crayon_body(", "),
crayon_key("receptor.complex"),
crayon_body(".")))
}
# Define legend parameters. Width and height values will change depending on the legend orientation.
if (legend.position %in% c("top", "bottom")){
size_title <- "Interaction specificity"
fill.title <- "Expression Magnitude"
} else if (legend.position %in% c("left", "right")){
size_title <- stringr::str_wrap("Interaction specificity", width = 10)
fill.title <- stringr::str_wrap("Expression Magnitude", width = 10)
}
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Column for specificity: "),
crayon_key(specificity)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Column for magnitude: "),
crayon_key(magnitude)))
}
liana_output <- liana_output %>%
dplyr::mutate("magnitude" = .data[[magnitude]]) %>%
dplyr::mutate("specificity" = .data[[specificity]])
invert_function <- function(x){-log10(x + 1e-10)}
if (isTRUE(invert_specificity)){
liana_output <- liana_output %>%
dplyr::mutate("specificity" := invert_function(x = .data$specificity))
}
if (isTRUE(invert_magnitude)){
liana_output <- liana_output %>%
dplyr::mutate("magnitude" := invert_function(.data$magnitude))
}
# Differential arrangement of the interactions.
liana_output <- liana_output %>%
# Merge ligand.complex and receptor.complex columns into one, that will be used for the Y axis.
tidyr::unite(c("ligand.complex", "receptor.complex"),
col = "interaction",
sep = " | ",
remove = FALSE) %>%
# Merge source and target column into one, for future filtering.
tidyr::unite(c("source", "target"),
col = "interacting_clusters",
remove = FALSE)
# For Chord diagrams.
output_copy <- liana_output %>% dplyr::filter(.data$aggregate_rank <= 0.05)
# If the user wants to trim the matrix and subset interacting entities.
if (!(is.null(keep_source))){
liana_output <- liana_output %>%
dplyr::filter(.data$source %in% keep_source)
output_copy <- output_copy %>%
dplyr::filter(.data$source %in% keep_source)
}
if (!(is.null(keep_target))){
liana_output <- liana_output %>%
dplyr::filter(.data$target %in% keep_target)
output_copy <- output_copy %>%
dplyr::filter(.data$target %in% keep_target)
}
# Sort interactions according to user's preference.
if (sort.by == "A"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("specificify")))
}
if (sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity))
} else {
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity)
}
} else if (sort.by == "B"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude")))
}
if (sorting.type.magnitude == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude))
} else {
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude)
}
} else if (sort.by == "C"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("specificify"),
crayon_body(" then "),
crayon_key("magnitude"),
crayon_body(".")))
}
if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity, .data$magnitude)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity, dplyr::desc(.data$magnitude))
} else if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity), .data$magnitude)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity), dplyr::desc(.data$magnitude))
}
} else if (sort.by == "D"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude"),
crayon_body(" then "),
crayon_key("specificity"),
crayon_body(".")))
}
if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude, .data$specificity)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude, dplyr::desc(.data$specificity))
} else if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude), .data$specificity)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude), dplyr::desc(.data$specificity))
}
} else if (sort.by == "E"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude"),
crayon_body(" and "),
crayon_key("specificity"),
crayon_body(" with equal weights.")))
}
if (sorting.type.magnitude == "ascending"){
liana_output_magnitude <- liana_output %>%
dplyr::arrange(.data$magnitude) %>%
tibble::rowid_to_column(var = "magnitude_rank")
} else {
liana_output_magnitude <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude)) %>%
tibble::rowid_to_column(var = "magnitude_rank")
}
if (sorting.type.specificity == "ascending"){
liana_output_specificity <- liana_output %>%
dplyr::arrange(.data$specificity) %>%
tibble::rowid_to_column(var = "specificity_rank")
} else {
liana_output_specificity <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity)) %>%
tibble::rowid_to_column(var = "specificity_rank")
}
liana_output <- liana_output %>%
dplyr::left_join(y = liana_output_specificity %>% dplyr::select(dplyr::all_of(c("interaction", "specificity_rank"))),
by = "interaction",
relationship = "many-to-many") %>%
dplyr::left_join(y = liana_output_magnitude %>% dplyr::select(dplyr::all_of(c("interaction", "magnitude_rank"))),
by = "interaction",
relationship = "many-to-many") %>%
dplyr::mutate("rank" = .data$magnitude_rank + .data$specificity_rank) %>%
dplyr::arrange(.data$rank) %>%
dplyr::select(!dplyr::all_of(c("rank", "magnitude_rank", "specificity_rank")))
rm(liana_output_magnitude)
rm(liana_output_specificity)
}
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting type specificity: "),
crayon_key(sorting.type.specificity)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting type magnitude: "),
crayon_key(sorting.type.magnitude)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Plotting the following top interanctions: "),
crayon_key(top_interactions)))
}
if (base::isFALSE(top_interactions_by_group)){
liana_output <- liana_output %>%
# Filter based on the top X interactions of ascending sensibilities.
dplyr::inner_join(y = {liana_output %>%
dplyr::distinct_at(c("ligand.complex", "receptor.complex")) %>%
dplyr::slice_head(n = top_interactions)},
by = c("ligand.complex", "receptor.complex"),
relationship = "many-to-many")
} else {
liana_output <- liana_output %>%
# Filter based on the top X interactions of ascending sensibilities.
dplyr::inner_join(y = {liana_output %>%
dplyr::group_by(.data$source, .data$target) %>%
dplyr::slice_head(n = top_interactions)},
by = c("ligand.complex", "receptor.complex"),
relationship = "many-to-many")
}
assertthat::assert_that(nrow(liana_output) > 0,
msg = paste0(add_cross(), crayon_body("Whith the current presets of "),
crayon_key("keep_source"),
crayon_body(" and "),
crayon_key("keep_target"),
crayon_body(" there are no interactions left.")))
# Make source and target factors, so that they do not get dropped by the plot.
if (isTRUE(sort_interactions_alphabetically)){
liana_output$source <- factor(liana_output$source, levels = sort(unique(liana_output$source)))
liana_output$target <- factor(liana_output$target, levels = sort(unique(liana_output$target)))
liana_output$interaction <- factor(liana_output$interaction, levels = rev(sort(unique(liana_output$interaction))))
} else if (base::isFALSE(sort_interactions_alphabetically)){
liana_output$source <- factor(liana_output$source, levels = sort(unique(liana_output$source)))
liana_output$target <- factor(liana_output$target, levels = sort(unique(liana_output$target)))
liana_output$interaction <- factor(liana_output$interaction, levels = rev(unique(liana_output$interaction)))
}
# Plot.
if (isTRUE(dot_border)){
p <- liana_output %>%
ggplot2::ggplot(mapping = ggplot2::aes(x = .data$target,
y = .data$interaction,
fill = .data$magnitude,
size = .data$specificity,
group = .data$interacting_clusters)) +
ggplot2::geom_point(shape = 21,
na.rm = TRUE)
} else if (base::isFALSE(dot_border)){
p <- liana_output %>%
ggplot2::ggplot(mapping = ggplot2::aes(x = .data$target,
y = .data$interaction,
size = .data$specificity,
group = .data$interacting_clusters)) +
ggplot2::geom_point(mapping = ggplot2::aes(color = .data$magnitude),
shape = 19,
na.rm = TRUE)
}
p <- p +
ggplot2::guides(y.sec = guide_axis_label_trans(~paste0(levels(.data$interaction)))) +
ggplot2::scale_size_continuous(name = size_title,
range = c(2 * dot.size, 10 * dot.size))
# Settings for bordered dots.
limits <- c(min(liana_output$magnitude, na.rm = TRUE),
max(liana_output$magnitude, na.rm = TRUE))
scale.setup <- compute_scales(sample = NULL,
feature = NULL,
assay = NULL,
reduction = NULL,
slot = NULL,
number.breaks = number.breaks,
min.cutoff = NA,
max.cutoff = NA,
flavor = "Seurat",
enforce_symmetry = FALSE,
from_data = TRUE,
limits.use = limits)
if (isTRUE(dot_border)){
# Add color to aesthetics.
p$layers[[1]]$aes_params$color <- border.color
p <- p +
ggplot2::scale_fill_gradientn(colors = colors.gradient,
na.value = NA,
name = fill.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits)
} else {
p <- p +
ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = NA,
name = fill.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits)
}
# Continue plotting.
if (is.null(split.by)){
p <- p +
ggplot2::facet_grid(. ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
} else if (split.by == "ligand.complex"){
p <- p +
ggplot2::facet_grid(.data$ligand.complex ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
} else if (split.by == "receptor.complex"){
p <- p +
ggplot2::facet_grid(.data$receptor.complex ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
}
p <- p +
ggplot2::labs(title = "Source") +
ggplot2::xlab("Target") +
ggplot2::ylab(paste("Ligand", "|", "Receptor", sep = " ")) +
ggplot2::guides(size = ggplot2::guide_legend(title.position = "top",
title.hjust = 0.5,
override.aes = ggplot2::aes(fill = "black"))) +
ggplot2::theme_minimal(base_size = font.size) +
ggplot2::theme(plot.title = ggplot2::element_text(face = plot.title.face,
hjust = 0.5,
vjust = 0,
size = font.size),
plot.subtitle = ggplot2::element_text(face = plot.subtitle.face, hjust = 0),
plot.caption = ggplot2::element_text(face = plot.caption.face, hjust = 1),
legend.text = ggplot2::element_text(face = legend.text.face),
legend.title = ggplot2::element_text(face = legend.title.face),
plot.title.position = "panel",
plot.caption.position = "plot",
text = ggplot2::element_text(family = font.type),
legend.justification = "center",
legend.position = legend.position,
axis.title.x = ggplot2::element_text(color = "black", face = axis.title.face, hjust = 0.5),
axis.title.y.left = ggplot2::element_text(color = "black", face = axis.title.face, angle = 90),
axis.title.y.right = ggplot2::element_blank(),
axis.text.y.right = ggplot2::element_text(color = "black",
face = axis.text.face),
axis.text.y.left = ggplot2::element_blank(),
axis.ticks.x = ggplot2::element_line(color = "black"),
axis.ticks.y.left = ggplot2::element_blank(),
axis.ticks.y.right = ggplot2::element_line(color = "black"),
axis.text.x = ggplot2::element_text(color = "black",
face = axis.text.face,
angle = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["angle"]],
hjust = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["hjust"]],
vjust = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["vjust"]]),
strip.text.x = ggplot2::element_text(face = "bold",
color = "black"),
strip.text.y = ggplot2::element_blank(),
panel.border = ggplot2::element_rect(color = "black", fill = NA),
panel.grid = if (isTRUE(plot.grid)){ggplot2::element_line(color = grid.color, linetype = grid.type)} else {ggplot2::element_blank()},
plot.margin = ggplot2::margin(t = 10, r = 10, b = 10, l = 10),
plot.background = ggplot2::element_rect(fill = "white", color = "white"),
panel.background = ggplot2::element_rect(fill = "white", color = "black", linetype = "solid"),
legend.background = ggplot2::element_rect(fill = "white", color = "white"))
# Adjust for the type of legend and whether it is fill or color.
p <- modify_continuous_legend(p = p,
legend.aes = ifelse(isTRUE(dot_border), "fill", "color"),
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
if (isTRUE(return_interactions)){
data_interactions <- output_copy %>%
dplyr::select(dplyr::all_of(c("source", "target"))) %>%
dplyr::group_by(.data$target, .data$source) %>%
dplyr::summarise(value = dplyr::n()) %>%
dplyr::rename("from" = "source",
"to" = "target") %>%
dplyr::select(dplyr::all_of(c("from", "to", "value")))
data_LF <- liana_output %>%
dplyr::filter(!(is.na(.data$magnitude))) %>%
dplyr::select(dplyr::all_of(c("ligand.complex", "receptor.complex"))) %>%
dplyr::group_by(.data$ligand.complex, .data$receptor.complex) %>%
dplyr::summarise(value = dplyr::n()) %>%
dplyr::rename("from" = "ligand.complex",
"to" = "receptor.complex") %>%
dplyr::select(dplyr::all_of(c("from", "to", "value")))
return(list("Plot" = p,
"Group Interactions" = data_interactions,
"LR Interactions" = data_LF))
} else {
return(p)
}
}
enblacar/SCpubr documentation built on Aug. 25, 2024, 9:45 p.m.
R Package Documentation
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Defines functions do_LigandReceptorPlot
Documented in do_LigandReceptorPlot
#' Visualize Ligand-Receptor analysis output.
#'
#' This function makes use of [liana](https://github.com/saezlab/liana) package to run Ligand-Receptor analysis. Takes the output of liana and generates a dot-plot visualization according to the user's specifications.
#'
#' @inheritParams doc_function
#' @param liana_output \strong{\code{\link[tibble]{tibble}}} | Object resulting from running \link[liana]{liana_wrap} and \link[liana]{liana_aggregate}.
#' @param split.by \strong{\code{\link[base]{character}}} | Whether to further facet the plot on the y axis by common ligand.complex or receptor.complex. Values to provide: NULL, ligand.complex, receptor.complex.
#' @param keep_source,keep_target \strong{\code{\link[base]{character}}} | Identities to keep for the source/target of the interactions. NULL otherwise.
#' @param top_interactions \strong{\code{\link[base]{numeric}}} | Number of unique interactions to retrieve ordered by magnitude and specificity. It does not necessarily mean that the output will contain as many, but rather an approximate value.
#' @param top_interactions_by_group \strong{\code{\link[base]{logical}}} | Enforce the value on \strong{\code{top_interactions}} to be applied to each group in \strong{\code{source}} column.
#' @param dot_border \strong{\code{\link[base]{logical}}} | Whether to draw a black border in the dots.
#' @param dot.size \strong{\code{\link[base]{numeric}}} | Size aesthetic for the dots.
#' @param sort.by \strong{\code{\link[base]{character}}} | How to arrange the top interactions. Interactions are sorted and then the top N are retrieved and displayed. This takes place after subsetting for \strong{\code{keep_source}} and \strong{\code{keep_target}} One of:
#' \itemize{
#' \item \emph{\code{A}}: Sorts by specificity.
#' \item \emph{\code{B}}: Sorts by magnitude.
#' \item \emph{\code{C}}: Sorts by specificity, then magnitude (gives extra weight to specificity).
#' \item \emph{\code{D}}: Sorts by magnitude, then specificity (gives extra weight to magnitude). Might lead to the display of non-significant results.
#' \item \emph{\code{E}}: Sorts by specificity and magnitude equally.
#' }
#' @param specificity,magnitude \strong{\code{\link[base]{character}}} | Which columns to use for \strong{\code{specificity}} and \strong{\code{magnitude}}.
#' @param invert_specificity,invert_magnitude \strong{\code{\link[base]{logical}}} | Whether to \strong{\code{-log10}} transform \strong{\code{specificity}} and \strong{\code{magnitude}} columns.
#' @param sorting.type.specificity,sorting.type.magnitude \strong{\code{\link[base]{character}}} | Whether the sorting of e \strong{\code{magnitude}} or \strong{\code{specificity}} columns is done in ascending or descending order. This synergises with the value of e \strong{\code{invert_specificity}} and e \strong{\code{invert_magnitude}} parameters.
#' @param compute_ChordDiagrams \strong{\code{\link[base]{logical}}} | Whether to also compute Chord Diagrams for both the number of interactions between source and target but also between ligand.complex and receptor.complex.
#' @param sort_interactions_alphabetically \strong{\code{\link[base]{logical}}} | Sort the interactions to be plotted alphabetically (\strong{\code{TRUE}}) or keep them in their original order in the matrix (\strong{\code{FALSE}}).
#' @param return_interactions \strong{\code{\link[base]{logical}}} | Whether to return the data.frames with the interactions so that they can be plotted as chord plots using other package functions.
#'
#' @return A ggplot2 plot with the results of the Ligand-Receptor analysis.
#' @export
#'
#' @example /man/examples/examples_do_LigandReceptorPlot.R
do_LigandReceptorPlot <- function(liana_output,
split.by = NULL,
keep_source = NULL,
keep_target = NULL,
top_interactions = 25,
top_interactions_by_group = FALSE,
dot_border = TRUE,
magnitude = "sca.LRscore",
specificity = "aggregate_rank",
sort.by = "E",
sorting.type.specificity = "descending",
sorting.type.magnitude = "descending",
border.color = "black",
axis.text.x.angle = 45,
legend.position = "bottom",
legend.type = "colorbar",
legend.length = 20,
legend.width = 1,
legend.framecolor = "grey50",
legend.tickcolor = "white",
legend.framewidth = 0.5,
legend.tickwidth = 0.5,
use_viridis = FALSE,
viridis.palette = "G",
viridis.direction = 1,
sequential.palette = "YlGnBu",
sequential.direction = 1,
font.size = 14,
dot.size = 1,
font.type = "sans",
plot.grid = TRUE,
grid.color = "grey90",
grid.type = "dotted",
compute_ChordDiagrams = FALSE,
sort_interactions_alphabetically = FALSE,
number.breaks = 5,
plot.title.face = "bold",
plot.subtitle.face = "plain",
plot.caption.face = "italic",
axis.title.face = "bold",
axis.text.face = "plain",
legend.title.face = "bold",
legend.text.face = "plain",
return_interactions = FALSE,
invert_specificity = TRUE,
invert_magnitude = FALSE,
verbose = TRUE){
# Add lengthy error messages.
withr::local_options(.new = list("warning.length" = 8170))
# Checks for packages.
check_suggests(function_name = "do_LigandReceptorPlot")
`%>%` <- magrittr::`%>%`
`:=` <- rlang::`:=`
# Check logical parameters.
logical_list <- list("dot_border" = dot_border,
"plot.grid" = plot.grid,
"sort_interactions_alphabetically" = sort_interactions_alphabetically,
"use_viridis" = use_viridis,
"return_interactions" = return_interactions,
"invert_specificity" = invert_specificity,
"invert_magnitude" = invert_magnitude,
"verbose" = verbose,
"top_interactions_by_group" = top_interactions_by_group)
check_type(parameters = logical_list, required_type = "logical", test_function = is.logical)
# Check numeric parameters.
numeric_list <- list("font.size" = font.size,
"top_interactions" = top_interactions,
"legend.length" = legend.length,
"legend.width" = legend.width,
"legend.framewidth" = legend.framewidth,
"legend.tickwidth" = legend.tickwidth,
"dot.size" = dot.size,
"axis.text.x.angle" = axis.text.x.angle,
"viridis.direction" = viridis.direction,
"number.breaks" = number.breaks,
"sequential.direction" = sequential.direction)
check_type(parameters = numeric_list, required_type = "numeric", test_function = is.numeric)
# Check character parameters.
character_list <- list("split.by" = split.by,
"keep_source" = keep_source,
"keep_target" = keep_target,
"border.color" = border.color,
"legend.position" = legend.position,
"legend.type" = legend.type,
"legend.framecolor" = legend.framecolor,
"viridis.palette" = viridis.palette,
"legend.tickcolor" = legend.tickcolor,
"font.type" = font.type,
"grid.color" = grid.color,
"grid.type" = grid.type,
"sequential.palette" = sequential.palette,
"plot.title.face" = plot.title.face,
"plot.subtitle.face" = plot.subtitle.face,
"plot.caption.face" = plot.caption.face,
"axis.title.face" = axis.title.face,
"axis.text.face" = axis.text.face,
"legend.title.face" = legend.title.face,
"legend.text.face" = legend.text.face,
"sort.by" = sort.by,
"sorting.type.specificity" = sorting.type.specificity,
"sorting.type.magnitude" = sorting.type.magnitude)
check_type(parameters = character_list, required_type = "character", test_function = is.character)
# Check border color.
check_colors(border.color, parameter_name = "border.color")
# Check the colors provided to legend.framecolor and legend.tickcolor.
check_colors(legend.framecolor, parameter_name = "legend.framecolor")
check_colors(legend.tickcolor, parameter_name = "legend.tickcolor")
check_colors(grid.color, parameter_name = "grid.color")
check_parameters(parameter = font.type, parameter_name = "font.type")
check_parameters(parameter = legend.type, parameter_name = "legend.type")
check_parameters(parameter = legend.position, parameter_name = "legend.position")
check_parameters(parameter = viridis.palette, parameter_name = "viridis.palette")
check_parameters(parameter = grid.type, parameter_name = "grid.type")
check_parameters(parameter = axis.text.x.angle, parameter_name = "axis.text.x.angle")
check_parameters(parameter = number.breaks, parameter_name = "number.breaks")
check_parameters(plot.title.face, parameter_name = "plot.title.face")
check_parameters(plot.subtitle.face, parameter_name = "plot.subtitle.face")
check_parameters(plot.caption.face, parameter_name = "plot.caption.face")
check_parameters(axis.title.face, parameter_name = "axis.title.face")
check_parameters(axis.text.face, parameter_name = "axis.text.face")
check_parameters(legend.title.face, parameter_name = "legend.title.face")
check_parameters(legend.text.face, parameter_name = "legend.text.face")
check_parameters(viridis.direction, parameter_name = "viridis.direction")
check_parameters(sequential.direction, parameter_name = "sequential.direction")
colors.gradient <- compute_continuous_palette(name = ifelse(isTRUE(use_viridis), viridis.palette, sequential.palette),
use_viridis = use_viridis,
direction = ifelse(isTRUE(use_viridis), viridis.direction, sequential.direction),
enforce_symmetry = FALSE)
if (!is.null(split.by)){
assertthat::assert_that(split.by %in% c("receptor.complex", "ligand.complex"),
msg = paste0(add_cross,
crayon_body("Please select one of the following for "),
crayon_key("split.by"),
crayon_body(": "),
crayon_key("ligand.complex"),
crayon_body(", "),
crayon_key("receptor.complex"),
crayon_body(".")))
}
# Define legend parameters. Width and height values will change depending on the legend orientation.
if (legend.position %in% c("top", "bottom")){
size_title <- "Interaction specificity"
fill.title <- "Expression Magnitude"
} else if (legend.position %in% c("left", "right")){
size_title <- stringr::str_wrap("Interaction specificity", width = 10)
fill.title <- stringr::str_wrap("Expression Magnitude", width = 10)
}
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Column for specificity: "),
crayon_key(specificity)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Column for magnitude: "),
crayon_key(magnitude)))
}
liana_output <- liana_output %>%
dplyr::mutate("magnitude" = .data[[magnitude]]) %>%
dplyr::mutate("specificity" = .data[[specificity]])
invert_function <- function(x){-log10(x + 1e-10)}
if (isTRUE(invert_specificity)){
liana_output <- liana_output %>%
dplyr::mutate("specificity" := invert_function(x = .data$specificity))
}
if (isTRUE(invert_magnitude)){
liana_output <- liana_output %>%
dplyr::mutate("magnitude" := invert_function(.data$magnitude))
}
# Differential arrangement of the interactions.
liana_output <- liana_output %>%
# Merge ligand.complex and receptor.complex columns into one, that will be used for the Y axis.
tidyr::unite(c("ligand.complex", "receptor.complex"),
col = "interaction",
sep = " | ",
remove = FALSE) %>%
# Merge source and target column into one, for future filtering.
tidyr::unite(c("source", "target"),
col = "interacting_clusters",
remove = FALSE)
# For Chord diagrams.
output_copy <- liana_output %>% dplyr::filter(.data$aggregate_rank <= 0.05)
# If the user wants to trim the matrix and subset interacting entities.
if (!(is.null(keep_source))){
liana_output <- liana_output %>%
dplyr::filter(.data$source %in% keep_source)
output_copy <- output_copy %>%
dplyr::filter(.data$source %in% keep_source)
}
if (!(is.null(keep_target))){
liana_output <- liana_output %>%
dplyr::filter(.data$target %in% keep_target)
output_copy <- output_copy %>%
dplyr::filter(.data$target %in% keep_target)
}
# Sort interactions according to user's preference.
if (sort.by == "A"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("specificify")))
}
if (sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity))
} else {
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity)
}
} else if (sort.by == "B"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude")))
}
if (sorting.type.magnitude == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude))
} else {
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude)
}
} else if (sort.by == "C"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("specificify"),
crayon_body(" then "),
crayon_key("magnitude"),
crayon_body(".")))
}
if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity, .data$magnitude)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$specificity, dplyr::desc(.data$magnitude))
} else if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity), .data$magnitude)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity), dplyr::desc(.data$magnitude))
}
} else if (sort.by == "D"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude"),
crayon_body(" then "),
crayon_key("specificity"),
crayon_body(".")))
}
if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude, .data$specificity)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "ascending"){
liana_output <- liana_output %>%
dplyr::arrange(.data$magnitude, dplyr::desc(.data$specificity))
} else if (sorting.type.magnitude == "ascending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude), .data$specificity)
} else if (sorting.type.magnitude == "descending" & sorting.type.specificity == "descending"){
liana_output <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude), dplyr::desc(.data$specificity))
}
} else if (sort.by == "E"){
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting interactions by: "),
crayon_key("magnitude"),
crayon_body(" and "),
crayon_key("specificity"),
crayon_body(" with equal weights.")))
}
if (sorting.type.magnitude == "ascending"){
liana_output_magnitude <- liana_output %>%
dplyr::arrange(.data$magnitude) %>%
tibble::rowid_to_column(var = "magnitude_rank")
} else {
liana_output_magnitude <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$magnitude)) %>%
tibble::rowid_to_column(var = "magnitude_rank")
}
if (sorting.type.specificity == "ascending"){
liana_output_specificity <- liana_output %>%
dplyr::arrange(.data$specificity) %>%
tibble::rowid_to_column(var = "specificity_rank")
} else {
liana_output_specificity <- liana_output %>%
dplyr::arrange(dplyr::desc(.data$specificity)) %>%
tibble::rowid_to_column(var = "specificity_rank")
}
liana_output <- liana_output %>%
dplyr::left_join(y = liana_output_specificity %>% dplyr::select(dplyr::all_of(c("interaction", "specificity_rank"))),
by = "interaction",
relationship = "many-to-many") %>%
dplyr::left_join(y = liana_output_magnitude %>% dplyr::select(dplyr::all_of(c("interaction", "magnitude_rank"))),
by = "interaction",
relationship = "many-to-many") %>%
dplyr::mutate("rank" = .data$magnitude_rank + .data$specificity_rank) %>%
dplyr::arrange(.data$rank) %>%
dplyr::select(!dplyr::all_of(c("rank", "magnitude_rank", "specificity_rank")))
rm(liana_output_magnitude)
rm(liana_output_specificity)
}
if (isTRUE(verbose)){
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting type specificity: "),
crayon_key(sorting.type.specificity)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Sorting type magnitude: "),
crayon_key(sorting.type.magnitude)))
rlang::inform(paste0(add_info(initial_newline = FALSE),
crayon_body("Plotting the following top interanctions: "),
crayon_key(top_interactions)))
}
if (base::isFALSE(top_interactions_by_group)){
liana_output <- liana_output %>%
# Filter based on the top X interactions of ascending sensibilities.
dplyr::inner_join(y = {liana_output %>%
dplyr::distinct_at(c("ligand.complex", "receptor.complex")) %>%
dplyr::slice_head(n = top_interactions)},
by = c("ligand.complex", "receptor.complex"),
relationship = "many-to-many")
} else {
liana_output <- liana_output %>%
# Filter based on the top X interactions of ascending sensibilities.
dplyr::inner_join(y = {liana_output %>%
dplyr::group_by(.data$source, .data$target) %>%
dplyr::slice_head(n = top_interactions)},
by = c("ligand.complex", "receptor.complex"),
relationship = "many-to-many")
}
assertthat::assert_that(nrow(liana_output) > 0,
msg = paste0(add_cross(), crayon_body("Whith the current presets of "),
crayon_key("keep_source"),
crayon_body(" and "),
crayon_key("keep_target"),
crayon_body(" there are no interactions left.")))
# Make source and target factors, so that they do not get dropped by the plot.
if (isTRUE(sort_interactions_alphabetically)){
liana_output$source <- factor(liana_output$source, levels = sort(unique(liana_output$source)))
liana_output$target <- factor(liana_output$target, levels = sort(unique(liana_output$target)))
liana_output$interaction <- factor(liana_output$interaction, levels = rev(sort(unique(liana_output$interaction))))
} else if (base::isFALSE(sort_interactions_alphabetically)){
liana_output$source <- factor(liana_output$source, levels = sort(unique(liana_output$source)))
liana_output$target <- factor(liana_output$target, levels = sort(unique(liana_output$target)))
liana_output$interaction <- factor(liana_output$interaction, levels = rev(unique(liana_output$interaction)))
}
# Plot.
if (isTRUE(dot_border)){
p <- liana_output %>%
ggplot2::ggplot(mapping = ggplot2::aes(x = .data$target,
y = .data$interaction,
fill = .data$magnitude,
size = .data$specificity,
group = .data$interacting_clusters)) +
ggplot2::geom_point(shape = 21,
na.rm = TRUE)
} else if (base::isFALSE(dot_border)){
p <- liana_output %>%
ggplot2::ggplot(mapping = ggplot2::aes(x = .data$target,
y = .data$interaction,
size = .data$specificity,
group = .data$interacting_clusters)) +
ggplot2::geom_point(mapping = ggplot2::aes(color = .data$magnitude),
shape = 19,
na.rm = TRUE)
}
p <- p +
ggplot2::guides(y.sec = guide_axis_label_trans(~paste0(levels(.data$interaction)))) +
ggplot2::scale_size_continuous(name = size_title,
range = c(2 * dot.size, 10 * dot.size))
# Settings for bordered dots.
limits <- c(min(liana_output$magnitude, na.rm = TRUE),
max(liana_output$magnitude, na.rm = TRUE))
scale.setup <- compute_scales(sample = NULL,
feature = NULL,
assay = NULL,
reduction = NULL,
slot = NULL,
number.breaks = number.breaks,
min.cutoff = NA,
max.cutoff = NA,
flavor = "Seurat",
enforce_symmetry = FALSE,
from_data = TRUE,
limits.use = limits)
if (isTRUE(dot_border)){
# Add color to aesthetics.
p$layers[[1]]$aes_params$color <- border.color
p <- p +
ggplot2::scale_fill_gradientn(colors = colors.gradient,
na.value = NA,
name = fill.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits)
} else {
p <- p +
ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = NA,
name = fill.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits)
}
# Continue plotting.
if (is.null(split.by)){
p <- p +
ggplot2::facet_grid(. ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
} else if (split.by == "ligand.complex"){
p <- p +
ggplot2::facet_grid(.data$ligand.complex ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
} else if (split.by == "receptor.complex"){
p <- p +
ggplot2::facet_grid(.data$receptor.complex ~ .data$source,
space = "free",
scales = "free",
drop = FALSE)
}
p <- p +
ggplot2::labs(title = "Source") +
ggplot2::xlab("Target") +
ggplot2::ylab(paste("Ligand", "|", "Receptor", sep = " ")) +
ggplot2::guides(size = ggplot2::guide_legend(title.position = "top",
title.hjust = 0.5,
override.aes = ggplot2::aes(fill = "black"))) +
ggplot2::theme_minimal(base_size = font.size) +
ggplot2::theme(plot.title = ggplot2::element_text(face = plot.title.face,
hjust = 0.5,
vjust = 0,
size = font.size),
plot.subtitle = ggplot2::element_text(face = plot.subtitle.face, hjust = 0),
plot.caption = ggplot2::element_text(face = plot.caption.face, hjust = 1),
legend.text = ggplot2::element_text(face = legend.text.face),
legend.title = ggplot2::element_text(face = legend.title.face),
plot.title.position = "panel",
plot.caption.position = "plot",
text = ggplot2::element_text(family = font.type),
legend.justification = "center",
legend.position = legend.position,
axis.title.x = ggplot2::element_text(color = "black", face = axis.title.face, hjust = 0.5),
axis.title.y.left = ggplot2::element_text(color = "black", face = axis.title.face, angle = 90),
axis.title.y.right = ggplot2::element_blank(),
axis.text.y.right = ggplot2::element_text(color = "black",
face = axis.text.face),
axis.text.y.left = ggplot2::element_blank(),
axis.ticks.x = ggplot2::element_line(color = "black"),
axis.ticks.y.left = ggplot2::element_blank(),
axis.ticks.y.right = ggplot2::element_line(color = "black"),
axis.text.x = ggplot2::element_text(color = "black",
face = axis.text.face,
angle = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["angle"]],
hjust = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["hjust"]],
vjust = get_axis_parameters(angle = axis.text.x.angle, flip = FALSE)[["vjust"]]),
strip.text.x = ggplot2::element_text(face = "bold",
color = "black"),
strip.text.y = ggplot2::element_blank(),
panel.border = ggplot2::element_rect(color = "black", fill = NA),
panel.grid = if (isTRUE(plot.grid)){ggplot2::element_line(color = grid.color, linetype = grid.type)} else {ggplot2::element_blank()},
plot.margin = ggplot2::margin(t = 10, r = 10, b = 10, l = 10),
plot.background = ggplot2::element_rect(fill = "white", color = "white"),
panel.background = ggplot2::element_rect(fill = "white", color = "black", linetype = "solid"),
legend.background = ggplot2::element_rect(fill = "white", color = "white"))
# Adjust for the type of legend and whether it is fill or color.
p <- modify_continuous_legend(p = p,
legend.aes = ifelse(isTRUE(dot_border), "fill", "color"),
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
if (isTRUE(return_interactions)){
data_interactions <- output_copy %>%
dplyr::select(dplyr::all_of(c("source", "target"))) %>%
dplyr::group_by(.data$target, .data$source) %>%
dplyr::summarise(value = dplyr::n()) %>%
dplyr::rename("from" = "source",
"to" = "target") %>%
dplyr::select(dplyr::all_of(c("from", "to", "value")))
data_LF <- liana_output %>%
dplyr::filter(!(is.na(.data$magnitude))) %>%
dplyr::select(dplyr::all_of(c("ligand.complex", "receptor.complex"))) %>%
dplyr::group_by(.data$ligand.complex, .data$receptor.complex) %>%
dplyr::summarise(value = dplyr::n()) %>%
dplyr::rename("from" = "ligand.complex",
"to" = "receptor.complex") %>%
dplyr::select(dplyr::all_of(c("from", "to", "value")))
return(list("Plot" = p,
"Group Interactions" = data_interactions,
"LR Interactions" = data_LF))
} else {
return(p)
}
}
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