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app.R
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app.R
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#' Generate a Shiny App for interactive exploration of CellChat's outputs
#'
#' @param object CellChat object
#' @param ... Other parameters of `shinyApp` function from shiny R package
#' @return A Shiny app object on the basis of one CellChat object
#' @export
#' @importFrom stringr str_split_1
# #' @importFrom plotly subplot plot_ly ggplotly add_markers highlight highlight_key plotlyOutput layout
# #' @importFrom bsicons bs_icon
#' @import shiny bslib
#'
runCellChatApp <- function(object,...) {
# ##########################################################################
# set some global options
# ##########################################################################
options(stringsAsFactors = FALSE)
# ##########################################################################
# some useful elements for ui.R
# ##########################################################################
choices_cell_groups <-levels(object@idents)
names(choices_cell_groups) <- levels(object@idents)
choices_pathways <- object@netP$pathways
names(choices_pathways) <- object@netP$pathways
# all signaling gene names
choices_gene_names <- CellChat::extractGene(object@DB)
# all ligand-receptor pair names
#choices_pairLR_use <- object@DB$interaction$interaction_name
if ("LRs" %in% names(object@net)) {
choices_pairLR_use <- object@net$LRs
} else {
thresh = 0.05
prob <- object@net$prob
prob[object@net$pval > thresh] <- 0
LR <- dimnames(prob)[[3]]
LR.sig <- LR[apply(prob, 3, sum) != 0]
choices_pairLR_use <- LR.sig
}
# Palettes (sequential)
choices_palettes_sequential <- stringr::str_split_1("Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd",", ")
names(choices_palettes_sequential) <- choices_palettes_sequential
choices_palettes_diverging <- stringr::str_split_1("BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral",", ")
names(choices_palettes_diverging) <- choices_palettes_diverging
# ##########################################################################
# interactive visualization
# ##########################################################################
# interactive Heatmap
# [Colors (ggplot2)](http:https://www.cookbook-r.com/Graphs/Colors_(ggplot2)/)
plotly_netVisual_heatmap <- function(obj_heatmap,palette.heatmap,direction.heatmap=1) {
gg_heatmap <- obj_heatmap@matrix %>%
as.data.frame() %>%
mutate(row = rownames(.)) %>%
tidyr::pivot_longer(
data = .,
cols = colnames(.)[-length(colnames(.))],
names_to = "column",
values_to = "value"
) %>%
ggplot() +
geom_tile(aes(row, column, fill = value),
width = 0.95,
height = 0.95) +
# guides(fill=guide_legend(title=obj_heatmap@row_title))+
labs(title = '',
x = '',
y = obj_heatmap@row_title,
# I can't set the direction of the legend title, I thick it's a bug
# fill = obj_heatmap@column_title,
) +
scale_fill_distiller(
palette = palette.heatmap,
na.value = 'white',
direction = direction.heatmap,
) +
theme_minimal()+
theme(axis.title.y = element_text(size = 14))
# ggplot transpose the matrix, so we need use colSums to calc the 'rowSums'
# of the matrix
gg_right <- obj_heatmap@matrix %>%
colSums(abs(.)) %>%
tibble(row_sum = ., sources_name = names(.)) %>%
ggplot() +
geom_bar(aes(x = sources_name, y = row_sum, fill = sources_name),
stat = 'identity') +
labs(title = '',
x = '',
y = '',) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "Set1", direction = 1) +
theme_minimal() +
coord_flip()
gg_top <- obj_heatmap@matrix %>%
rowSums(abs(.)) %>%
tibble(col_sum = ., sources_name = names(.)) %>%
ggplot() +
# use fill to set the columns' colors
geom_bar(aes(x = sources_name, y = col_sum, fill = sources_name),
stat = 'identity') +
labs(title = obj_heatmap@column_title,
x = '',
y = '',) +
guides(fill = FALSE)+
scale_fill_brewer(palette = "Set1", direction = 1) +
# theme() function should be used behind the theme_*()
theme_minimal()+
theme(plot.title = element_text(hjust = 0.5,size = 14))
return(plotly::subplot(
gg_top,
plotly::plotly_empty(),
gg_heatmap,
gg_right,
nrows = 2,
heights = c(0.2, 0.8),
widths = c(0.8, 0.2),
margin = 0,
shareX = TRUE,
shareY = TRUE,
titleX = TRUE,
titleY = TRUE
)
)
}
# interactive DimPlot
plotly_DimPlot <- function (object,
color.use = NULL,
group.by = NULL,
sample.use = NULL,
reduction = NULL,
sources.use = NULL,
targets.use = NULL,
idents.use = NULL,
alpha = 1,
title.name = NULL,
point.size = 1)
{
if (is.null(group.by)) {
labels <- object@idents
} else {
labels = object@meta[, group.by]
labels <- factor(labels)
}
if (length(names(object@dr)) == 0) {
stop("Please check `addReduction` to add a new reduced space into `object@dr`. \n")
}
if (!is.null(reduction)) {
coords <- object@dr[[reduction]]
} else {
if ("umap" %in% names(object@dr)) {
coords <- object@dr$umap
} else if ("tsne" %in% names(object@dr)){
coords <- object@dr$tsne
} else {
stop(paste0("The `object@dr` contains the following reduced space: ", toString(names(object@dr)), ". Please specify the dimensionality reduction to use. \n"))
}
}
coordinates <- as.data.frame(coords)
samples <- object@meta$samples
if (ncol(coordinates) >= 2) {
coordinates <- coordinates[, c(1,2)]
colnames(coordinates) <- c("x_cent","y_cent")
if (length(unique(samples)) > 1) {
if (is.null(sample.use)) {
stop("`sample.use` should be provided for visualizing signaling on each individual sample.")
} else if (sample.use %in% unique(samples)) {
coordinates = coordinates[samples == sample.use, ]
labels = labels[samples == sample.use]
} else {
stop("Please check the input `sample.use`, which should be the element in `meta$samples`.")
}
}
# temp_coordinates = coordinates
# coordinates[,1] = temp_coordinates[,2]
# coordinates[,2] = temp_coordinates[,1]
} else {
stop("Please check the input 'object@dr' and make sure it has at least two columns.")
}
cells.level <- levels(labels)
if (!is.null(idents.use)) {
if (is.numeric(idents.use)) {
idents.use <- cells.level[idents.use]
}
cell.use <- !(labels %in% idents.use)
labels[cell.use] <- NA
cells.level <- cells.level[cells.level %in% idents.use]
labels <- factor(labels, levels = cells.level)
}
if (is.null(sources.use) & is.null(targets.use)) {
if (is.null(color.use)) {
color.use <- scPalette(nlevels(labels))
}
}
else {
if (is.numeric(sources.use)) {
sources.use <- cells.level[sources.use]
}
if (is.numeric(targets.use)) {
targets.use <- cells.level[targets.use]
}
group <- rep("Others", length(labels))
group[(labels %in% sources.use)] <- sources.use
group[(labels %in% targets.use)] <- targets.use
group = factor(group, levels = c(sources.use, targets.use,
"Others"))
if (is.null(color.use)) {
color.use.all <- scPalette(nlevels(labels))
color.use <- color.use.all[match(c(sources.use,
targets.use), levels(labels))]
color.use[nlevels(group)] <- "grey90"
}
labels <- group
}
# print(color.use)->return a color vector
coordinates$cell_labels <- labels
py <- plotly::highlight_key(coordinates,~cell_labels) %>%
plotly::plot_ly(x = ~x_cent, y = ~y_cent,marker = list(size = point.size)) %>%
plotly::add_markers(color=~cell_labels,alpha=alpha,colors=color.use) %>%
plotly::layout(
title = title.name,
yaxis = list(
title = "",
#autorange = "reversed",
showgrid = FALSE,
ticks = "",
# ticktext = "",
tickvals = "",
zeroline = FALSE,
showline = FALSE
),
xaxis = list(
title = "",
showgrid = FALSE,
ticks = "",
# ticktext = "",
tickvals = "",
zeroline = FALSE,
showline = FALSE
)
) %>%
plotly::highlight(on = "plotly_click",
off = "plotly_relayout")
return(py)
}
# interactive FeaturePlot
# https://plotly.com/r/subplots/
plotly_FeaturePlot <- function (object,
features = NULL,
signaling = NULL,
pairLR.use = NULL,
sample.use = NULL,
reduction = NULL,
enriched.only = TRUE,
thresh = 0.05,
do.group = TRUE,
color.heatmap = "Reds",
n.colors = 8,
direction = -1,
do.binary = FALSE,
cutoff = NULL,
color.use = NULL,
alpha = 1,
point.size = 0.8,
legend.size = 3,
legend.text.size = 8,
shape.by = 16,
plot_nrows = 1,
show.legend = TRUE,
show.legend.combined = FALSE){
if (!is.null(reduction)) {
coords <- object@dr[[reduction]]
} else {
if ("umap" %in% names(object@dr)) {
coords <- object@dr$umap
} else if ("tsne" %in% names(object@dr)){
coords <- object@dr$tsne
} else {
stop("Please make sure `object@dr` contains a low-dimensional space of the data and specify the dimensionality reduction to use.")
}
}
samples <- object@meta$samples
cell_labels <- object@idents
data <- as.matrix(object@data)
meta <- object@meta
coords <- as.data.frame(coords)
if (ncol(coords) >= 2) {
coords <- coords[, c(1,2)]
colnames(coords) <- c("x_cent","y_cent")
if (length(unique(samples)) > 1) {
if (is.null(sample.use)) {
stop("`sample.use` should be provided for visualizing signaling on each individual sample.")
} else if (sample.use %in% unique(samples)) {
coords = coords[samples == sample.use, ]
meta = meta[samples == sample.use, ]
data = data[, samples == sample.use]
} else {
stop("Please check the input `sample.use`, which should be the element in `meta$samples`.")
}
}
} else {
stop("Please check the input 'object@dr' and make sure it has at least two columns.")
}
# add idents info
if (length(color.heatmap) == 1) {
colormap <- tryCatch({
RColorBrewer::brewer.pal(n = n.colors, name = color.heatmap)
}, error = function(e) {
(scales::viridis_pal(option = color.heatmap, direction = -1))(n.colors)
})
if (direction == -1) {
colormap <- rev(colormap)
}
colormap <- colorRampPalette(colormap)(99)
colormap[1] <- "#E5E5E5"
}
else {
colormap <- color.heatmap
}
if (is.null(features) &
is.null(signaling) & is.null(pairLR.use)) {
stop("Please input either features, signaling or pairLR.use.")
}
if (!is.null(features) & !is.null(signaling)) {
stop("Please don't input features or signaling simultaneously.")
}
if (!is.null(features) & !is.null(pairLR.use)) {
stop("Please don't input features or pairLR.use simultaneously.")
}
if (!is.null(signaling) & !is.null(pairLR.use)) {
stop("Please don't input signaling or pairLR.use simultaneously.")
}
df <- data.frame(x = coords[, 1], y = coords[, 2],
cell_labels = cell_labels)
if (!do.binary) {
if (!is.null(signaling)) {
res <- extractEnrichedLR(
object,
signaling = signaling,
geneLR.return = TRUE,
enriched.only = enriched.only,
thresh = thresh
)
feature.use <- res$geneLR
}
else if (!is.null(pairLR.use)) {
if (is.character(pairLR.use)) {
pairLR.use <- data.frame(interaction_name = pairLR.use)
}
if (enriched.only) {
if (do.group) {
object@net$prob[object@net$pval > thresh] <- 0
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob)[[3]]]
prob <- object@net$prob[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
else {
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob.cell)[[3]]]
prob.cell <- object@net$prob.cell[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob.cell > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
if (length(pairLR.use$interaction_name) == 0) {
stop(
paste0(
"There is no significant communication related with the input `pairLR.use`. Set `enriched.only = FALSE` to show non-significant signaling."
)
)
}
}
LR.pair <- object@LR$LRsig[pairLR.use$interaction_name,
c("ligand", "receptor")]
geneL <- unique(LR.pair$ligand)
geneR <- unique(LR.pair$receptor)
geneL <- extractGeneSubset(geneL, object@DB$complex,
object@DB$geneInfo)
geneR <- extractGeneSubset(geneR, object@DB$complex,
object@DB$geneInfo)
feature.use <- c(geneL, geneR)
}
else {
feature.use <- features
}
if (length(intersect(feature.use, rownames(data))) >
0) {
feature.use <- feature.use[feature.use %in% rownames(data)]
data.use <- data[feature.use, , drop = FALSE]
}
else if (length(intersect(feature.use, colnames(meta))) >
0) {
feature.use <- feature.use[feature.use %in% colnames(meta)]
data.use <- t(meta[, feature.use, drop = FALSE])
}
else {
stop("Please check your input! ")
}
if (!is.null(cutoff)) {
cat("Applying a cutoff of ", cutoff, "to the values...",
"\n")
data.use[data.use <= cutoff] <- 0
}
numFeature = length(feature.use)
gg <- vector("list", numFeature)
plot_ncols <- ceiling(numFeature / plot_nrows)
plot_width <- 1 / plot_ncols
plot_height <- 1 / plot_nrows
annotations_pos <- vector("list", 0)
for (i in 1:plot_nrows) {
for (j in 1:plot_ncols) {
x <- (j - 0.5) * plot_width
y <- 1-(i - 0.95) * plot_height
annotations_pos[[length(annotations_pos)+1]] <- list(
x=x,
y=y
)
}
}
annotations <- vector("list", numFeature)
for (i in seq_len(numFeature)) {
feature.name <- feature.use[i]
df$feature.data <- data.use[i,]
g <-
ggplot(data = df, aes(x, y)) + geom_point(
aes(colour = feature.data,cell_labels=cell_labels),
alpha = alpha,
size = point.size,
shape = shape.by
) +
scale_colour_gradientn(
colours = colormap,
guide = guide_colorbar(
title = NULL,
ticks = T,
label = T,
barwidth = 0.5
),
na.value = "grey90"
) +
theme(legend.position = "right") + theme(
legend.title = element_blank(),
legend.text = element_text(size = legend.text.size),
legend.key.size = unit(0.15, "inches")
) + ggtitle(feature.name) +
theme(plot.title = element_text(
hjust = 0.5,
vjust = 0,
size = 10
)) + theme(
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank()
) +
xlab(NULL) + ylab(NULL) + theme(legend.key = element_blank())
# g <- g + coord_fixed() +
# scale_y_reverse()
gg[[i]] <- g %>% plotly::ggplotly(height = 400)
annotations[[i]] <- list(
x=annotations_pos[[i]]$x,
y=annotations_pos[[i]]$y,
text = feature.name,
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "top",
showarrow = FALSE,
font = list(size = 16))
}
# print(annotations_pos)
if (numFeature > 1) {
gg <- plotly::subplot(gg, nrows = plot_nrows,margin = 0.02,shareX = FALSE,shareY = FALSE) %>%
plotly::layout(title = '',annotations = annotations)
}
else {
gg <- plotly::ggplotly(gg[[1]])
}
}
else {
# do.binary
set_individual_legend <- function(plt) {
# plt is a plotly plot obj
plt_build <- plotly::plotly_build(plt)
# get the num of traces
len_legend <- length(plt_build$x$data)
for (i in 1:len_legend) {
# set legendgroup
plt_build$x$data[[i]]$legendgroup <- feature.name
# set legendtitle
plt_build$x$data[[i]]$legendgrouptitle <- list(text=feature.name,font=list(size=12))
}
return(plt_build)
}
if (is.null(color.use)) {
color.use <- ggPalette(4)
color.use[4] <- "grey90"
}
color.use1 = color.use
if (!is.null(signaling)) {
res <- extractEnrichedLR(
object,
signaling = signaling,
enriched.only = enriched.only,
thresh = thresh
)
LR.pair <- object@LR$LRsig[res$interaction_name,
c("ligand", "receptor")]
}
else if (!is.null(pairLR.use)) {
if (is.character(pairLR.use)) {
pairLR.use <- data.frame(interaction_name = pairLR.use)
}
if (enriched.only) {
if (do.group) {
object@net$prob[object@net$pval > thresh] <- 0
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob)[[3]]]
prob <- object@net$prob[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
else {
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob.cell)[[3]]]
prob.cell <- object@net$prob.cell[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob.cell > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
if (length(pairLR.use$interaction_name) == 0) {
stop(
paste0(
"There is no significant communication related with the input `pairLR.use`. Set `enriched.only = FALSE` to show non-significant signaling."
)
)
}
}
LR.pair <- object@LR$LRsig[pairLR.use$interaction_name,
c("ligand", "receptor")]
}
else {
stop("Please input either `pairLR.use` or `signaling` for `binary` mode!")
}
geneL <- as.character(LR.pair$ligand)
geneR <- as.character(LR.pair$receptor)
complex_input <- object@DB$complex
dataL <- computeExpr_LR(geneL, data, complex_input)
dataR <- computeExpr_LR(geneR, data, complex_input)
rownames(dataL) <- geneL
rownames(dataR) <- geneR
feature.use <- rownames(LR.pair)
numFeature = nrow(LR.pair)
if (is.null(cutoff)) {
stop("A `cutoff` must be provided when plotting expression in binary mode! ")
}
gg <- vector("list", numFeature)
# set subplot title pos
plot_ncols <- ceiling(numFeature / plot_nrows)
plot_width <- 1 / plot_ncols
plot_height <- 1 / plot_nrows
annotations_pos <- vector("list", 0)
for (i in 1:plot_nrows) {
for (j in 1:plot_ncols) {
x <- (j - 0.5) * plot_width
y <- 1-(i - 1) * plot_height
annotations_pos[[length(annotations_pos)+1]] <- list(
x=x,
y=y
)
}
}
annotations <- vector("list", numFeature)
for (i in seq_len(numFeature)) {
feature.name <- feature.use[i]
idx1 = dataL[i,] > cutoff
idx2 = dataR[i,] > cutoff
idx3 = idx1 & idx2
group = rep("None", ncol(dataL))
group[idx1] = geneL[i]
group[idx2] = geneR[i]
group[idx3] = "Both"
group = factor(group, levels = c(geneL[i], geneR[i],
"Both", "None"))
color.use <- color.use1
names(color.use) <- c(geneL[i], geneR[i], "Both",
"None")
if (length(setdiff(levels(group), unique(group))) >
0) {
color.use <- color.use[names(color.use) %in% unique(group)]
group = droplevels(group, exclude = setdiff(levels(group),
unique(group)))
}
df$feature.data <- group
g <-
ggplot(data = df, aes(x, y)) + geom_point(
aes(colour = feature.data,cell_labels=cell_labels),
alpha = alpha,
size = point.size,
shape = shape.by
) +
scale_color_manual(values = color.use, na.value = "grey90") +
theme(legend.position = "right") + theme(
legend.title = element_blank(),
legend.text = element_text(size = legend.text.size),
legend.key.size = unit(0.15, "inches")
) + guides(color = guide_legend(override.aes = list(size = legend.size))) +
ggtitle(feature.name) + theme(plot.title = element_text(
hjust = 0.5,
vjust = 0,
size = 10
)) + theme(
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank()
) +
xlab(NULL) + ylab(NULL) + theme(legend.key = element_blank())
# g <- g + coord_fixed() +
# scale_y_reverse()
# cat(feature.name)
gg[[i]] <- g %>% plotly::ggplotly(
type = 'scatter',
mode='markers+text',
) %>% set_individual_legend()
annotations[[i]] <- list(
x=annotations_pos[[i]]$x,
y=annotations_pos[[i]]$y,
text = feature.name,
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "top",
showarrow = FALSE,
font = list(size = 16))
}
if (numFeature > 1) {
gg <- plotly::subplot(gg, nrows = plot_nrows,margin = 0.02,shareX = FALSE,shareY = FALSE) %>%
plotly::layout(title = '',
annotations = annotations,
legend = list(tracegroupgap = 10,title=list(text=''))
)
}
else {
gg <- plotly::ggplotly(gg[[1]],
type = 'scatter',
mode = 'markers') %>%
plotly::layout(legend = list(title = list(text = '')))
}
}
return(gg)
}
# interactive spatialDimPlot
plotly_spatialDimPlot <- function (object,
color.use = NULL,
group.by = NULL,
sample.use = NULL,
sources.use = NULL,
targets.use = NULL,
idents.use = NULL,
alpha = 1,
title.name = NULL,
point.size = 1)
{
if (is.null(group.by)) {
labels <- object@idents
} else {
labels = object@meta[, group.by]
labels <- factor(labels)
}
coordinates <- as.data.frame(object@images$coordinates)
samples <- object@meta$samples
if (ncol(coordinates) == 2) {
colnames(coordinates) <- c("x_cent","y_cent")
if (length(unique(samples)) > 1) {
if (is.null(sample.use)) {
stop("`sample.use` should be provided for visualizing signaling on each individual sample.")
} else if (sample.use %in% unique(samples)) {
coordinates = coordinates[samples == sample.use, ]
labels = labels[samples == sample.use]
} else {
stop("Please check the input `sample.use`, which should be the element in `meta$samples`.")
}
}
temp_coordinates = coordinates
coordinates[,1] = temp_coordinates[,2]
coordinates[,2] = temp_coordinates[,1]
} else {
stop("Please check the input 'coordinates' and make sure it is a two column matrix.")
}
cells.level <- levels(labels)
if (!is.null(idents.use)) {
if (is.numeric(idents.use)) {
idents.use <- cells.level[idents.use]
}
cell.use <- !(labels %in% idents.use)
labels[cell.use] <- NA
cells.level <- cells.level[cells.level %in% idents.use]
labels <- factor(labels, levels = cells.level)
}
if (is.null(sources.use) & is.null(targets.use)) {
if (is.null(color.use)) {
color.use <- scPalette(nlevels(labels))
}
}
else {
if (is.numeric(sources.use)) {
sources.use <- cells.level[sources.use]
}
if (is.numeric(targets.use)) {
targets.use <- cells.level[targets.use]
}
group <- rep("Others", length(labels))
group[(labels %in% sources.use)] <- sources.use
group[(labels %in% targets.use)] <- targets.use
group = factor(group, levels = c(sources.use, targets.use,
"Others"))
if (is.null(color.use)) {
color.use.all <- scPalette(nlevels(labels))
color.use <- color.use.all[match(c(sources.use,
targets.use), levels(labels))]
color.use[nlevels(group)] <- "grey90"
}
labels <- group
}
# print(color.use)->return a color vector
coordinates$cell_labels <- labels
py <- plotly::highlight_key(coordinates,~cell_labels) %>%
plotly::plot_ly(x = ~x_cent, y = ~y_cent,marker = list(size = point.size)) %>%
plotly::add_markers(color=~cell_labels,alpha=alpha,colors=color.use) %>%
plotly::layout(
title = title.name,
yaxis = list(
autorange = "reversed",
title = "",
showgrid = FALSE,
ticks = "",
# ticktext = "",
tickvals = "",
showline = FALSE
),
xaxis = list(
title = "",
showgrid = FALSE,
ticks = "",
# ticktext = "",
tickvals = "",
showline = FALSE
)
) %>%
plotly::highlight(on = "plotly_click",
off = "plotly_relayout")
return(py)
}
# interactive spatialFeaturePlot
# https://plotly.com/r/subplots/
plotly_spatialFeaturePlot <- function (object,
features = NULL,
signaling = NULL,
pairLR.use = NULL,
sample.use = NULL,
enriched.only = TRUE,
thresh = 0.05,
do.group = TRUE,
color.heatmap = "Reds",
n.colors = 8,
direction = -1,
do.binary = FALSE,
cutoff = NULL,
color.use = NULL,
alpha = 1,
point.size = 0.8,
legend.size = 3,
legend.text.size = 8,
shape.by = 16,
plot_nrows = 1,
show.legend = TRUE,
show.legend.combined = FALSE){
coords <- as.data.frame(object@images$coordinates)
samples <- object@meta$samples
cell_labels <- object@idents
data <- as.matrix(object@data)
meta <- object@meta
if (ncol(coords) == 2) {
colnames(coords) <- c("x_cent","y_cent")
if (length(unique(samples)) > 1) {
if (is.null(sample.use)) {
stop("`sample.use` should be provided for visualizing signaling on each individual sample.")
} else if (sample.use %in% unique(samples)) {
coords = coords[samples == sample.use, ]
meta = meta[samples == sample.use, ]
data = data[, samples == sample.use]
} else {
stop("Please check the input `sample.use`, which should be the element in `meta$samples`.")
}
}
temp_coords = coords
coords[,1] = temp_coords[,2]
coords[,2] = temp_coords[,1]
} else {
stop("Please check the input 'coordinates' and make sure it is a two column matrix.")
}
# add idents info
if (length(color.heatmap) == 1) {
colormap <- tryCatch({
RColorBrewer::brewer.pal(n = n.colors, name = color.heatmap)
}, error = function(e) {
(scales::viridis_pal(option = color.heatmap, direction = -1))(n.colors)
})
if (direction == -1) {
colormap <- rev(colormap)
}
colormap <- colorRampPalette(colormap)(99)
colormap[1] <- "#E5E5E5"
}
else {
colormap <- color.heatmap
}
if (is.null(features) &
is.null(signaling) & is.null(pairLR.use)) {
stop("Please input either features, signaling or pairLR.use.")
}
if (!is.null(features) & !is.null(signaling)) {
stop("Please don't input features or signaling simultaneously.")
}
if (!is.null(features) & !is.null(pairLR.use)) {
stop("Please don't input features or pairLR.use simultaneously.")
}
if (!is.null(signaling) & !is.null(pairLR.use)) {
stop("Please don't input signaling or pairLR.use simultaneously.")
}
df <- data.frame(x = coords[, 1], y = coords[, 2],
cell_labels = cell_labels)
if (!do.binary) {
if (!is.null(signaling)) {
res <- extractEnrichedLR(
object,
signaling = signaling,
geneLR.return = TRUE,
enriched.only = enriched.only,
thresh = thresh
)
feature.use <- res$geneLR
}
else if (!is.null(pairLR.use)) {
if (is.character(pairLR.use)) {
pairLR.use <- data.frame(interaction_name = pairLR.use)
}
if (enriched.only) {
if (do.group) {
object@net$prob[object@net$pval > thresh] <- 0
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob)[[3]]]
prob <- object@net$prob[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
else {
pairLR.use.name <-
pairLR.use$interaction_name[pairLR.use$interaction_name %in%
dimnames(object@net$prob.cell)[[3]]]
prob.cell <- object@net$prob.cell[, , pairLR.use.name,
drop = FALSE]
prob.sum <- apply(prob.cell > 0, 3, sum)
names(prob.sum) <- pairLR.use.name
signaling.includes <- names(prob.sum)[prob.sum >
0]
pairLR.use <- pairLR.use[pairLR.use$interaction_name %in%
signaling.includes, , drop = FALSE]
}
if (length(pairLR.use$interaction_name) == 0) {
stop(
paste0(
"There is no significant communication related with the input `pairLR.use`. Set `enriched.only = FALSE` to show non-significant signaling."
)
)
}
}
LR.pair <- object@LR$LRsig[pairLR.use$interaction_name,
c("ligand", "receptor")]
geneL <- unique(LR.pair$ligand)
geneR <- unique(LR.pair$receptor)
geneL <- extractGeneSubset(geneL, object@DB$complex,
object@DB$geneInfo)
geneR <- extractGeneSubset(geneR, object@DB$complex,
object@DB$geneInfo)
feature.use <- c(geneL, geneR)
}
else {
feature.use <- features
}
if (length(intersect(feature.use, rownames(data))) >
0) {
feature.use <- feature.use[feature.use %in% rownames(data)]
data.use <- data[feature.use, , drop = FALSE]
}
else if (length(intersect(feature.use, colnames(meta))) >
0) {
feature.use <- feature.use[feature.use %in% colnames(meta)]
data.use <- t(meta[, feature.use, drop = FALSE])
}
else {
stop("Please check your input! ")
}
if (!is.null(cutoff)) {
cat("Applying a cutoff of ", cutoff, "to the values...",
"\n")
data.use[data.use <= cutoff] <- 0
}
numFeature = length(feature.use)
gg <- vector("list", numFeature)
plot_ncols <- ceiling(numFeature / plot_nrows)
plot_width <- 1 / plot_ncols
plot_height <- 1 / plot_nrows
annotations_pos <- vector("list", 0)
for (i in 1:plot_nrows) {
for (j in 1:plot_ncols) {
x <- (j - 0.5) * plot_width
y <- 1-(i - 0.95) * plot_height
annotations_pos[[length(annotations_pos)+1]] <- list(
x=x,