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spatial_functions.R
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spatial_functions.R
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FeatureEmbedding <- function(
seurat_obj,
features,
raster = TRUE,
facet_by = NULL,
slot = 'data',
assay = NULL,
reduction = 'umap',
dpi = 200,
dpi_scale=0.5,
ncol = 4,
combine=TRUE,
order_points=TRUE, # 'shuffle'
point_size = 0.5,
plot_max = 'q100',
plot_min = 'q0',
same_range = FALSE,
colfunc = viridis::inferno,
rev_colors = TRUE
){
input_plot_max <- plot_max
input_plot_min <- plot_min
plot_list <- list()
if(same_range){
tmp <- GetAssayData(seurat_obj, slot=slot, assay=assay)[features,]
plot_range <- range(tmp)
print(plot_range)
if(is.null(plot_max)){plot_max <- plot_range[2]}
if(is.null(plot_min)){plot_min <- plot_range[1]}
print(plot_max)
print(plot_min)
}
for(feature in features){
plot_df <- [email protected]
plot_df$plot_x_coord <- seurat_obj@reductions[[reduction]]@cell.embeddings[,1]
plot_df$plot_y_coord <- seurat_obj@reductions[[reduction]]@cell.embeddings[,2]
# check if the feature is in the meta-data
if(feature %in% rownames(seurat_obj)){
if(is.null(assay)){assay <- [email protected]}
plot_df$plotfeature <- GetAssayData(seurat_obj, slot=slot, assay=assay)[feature,]
} else if(feature %in% colnames(plot_df)){
if(!is.numeric(plot_df[[feature]])){
stop("Specified feature is not numeric. Try plotting with VisDimPlot?")
}
plot_df$plotfeature <- plot_df[[feature]]
} else{
stop("feature not found in rownames(seurat_obj) or in colnames([email protected]).")
}
if(!same_range){
plot_max <- input_plot_max
plot_min <- input_plot_min
plot_range <- range(plot_df$plotfeature)
if(!is.null(plot_max)){
if(is.character(plot_max)){
quant <- as.numeric(gsub('q', '', plot_max)) / 100
plot_max <- as.numeric(quantile(plot_df$plotfeature, quant))
}
plot_range[2] <- plot_max
print(plot_max)
plot_df$plotfeature <- ifelse(
plot_df$plotfeature > plot_max,
plot_max,
plot_df$plotfeature
)
}
if(is.character(plot_min)){
quant <- as.numeric(gsub('q', '', plot_min)) / 100
plot_min <- as.numeric(quantile(plot_df$plotfeature, quant))
}
plot_range[1] <- plot_min
}
#
# shuffle points:
if(order_points == TRUE){
plot_df <- plot_df %>% dplyr::arrange(plotfeature)
} else if(order_points == "shuffle"){
plot_df <- plot_df[sample(nrow(plot_df)),]
}
# initialize gpgplot
p <- plot_df %>% subset(plotfeature > plot_min) %>%
ggplot(aes_(x=~plot_x_coord, y=~plot_y_coord,color=~plotfeature))
# add all the grey dots with low/zero expression
if(raster){
p <- p +
ggrastr::rasterise(
geom_point(inherit.aes=FALSE, data = subset(plot_df, plotfeature <= plot_min), aes(x=plot_x_coord, y=plot_y_coord),color='lightgrey', size=point_size),
dpi=dpi, scale=dpi_scale
) +
ggrastr::rasterise(
geom_point(size=point_size),
dpi=dpi, scale=dpi_scale
)
} else{
p <- p +
geom_point(inherit.aes=FALSE, data = subset(plot_df, plotfeature <= plot_min), aes(x=plot_x_coord, y=plot_y_coord),color='lightgrey', size=point_size) +
geom_point(size=point_size)
}
# add extras to plot:
colors <- colfunc(256)
if(rev_colors){colors <- rev(colors)}
p <- p +
labs(color = feature) +
scale_color_gradientn(colors=colors, limits = plot_range) +
coord_equal() +
theme(
plot.title = element_text(hjust=0.5, face='plain'),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank()
)
plot_list[[feature]] <- p
}
# if there's only one feature plotted
if(length(plot_list) == 1){
p <- plot_list[[1]]
# do we want to facet?
if(!is.null(facet_by)){
p <- p + facet_wrap( ~ get(facet_by), ncol=ncol)
}
return(p)
}
if(combine){
patch <- wrap_plots(plot_list, ncol=ncol)
return(patch)
} else{
return(plot_list)
}
}
custom_vln <- function(
seurat_obj,
features,
group.by,
split.by=NULL,
groups = NULL,
selected_split = NULL,
plot.margin = margin(0,0,0,0, "cm"),
slot = 'data',
assay = NULL,
raster_dpi=200,
add_boxplot = TRUE,
pt.size = 0,
add.noise = TRUE,
line.size=NULL,
adjust=1,
quantiles=c(0.5),
stat_method = 'wilcox.test',
comparisons = NULL,
pval_y_adjust=0.7,
ref_group = '.all.',
group_color_df = NULL,
split_colors = NULL,
add_colorbar = TRUE,
plot_ymin = 0,
plot_ymax = NULL
){
if(is.null(assay)){assay <- [email protected]}
seurat_meta <- [email protected]
if(class(seurat_meta[[group.by]]) != 'factor'){
seurat_meta[[group.by]] <- as.factor(seurat_meta[[group.by]])
}
# get list of groups from seurat object
if(is.null(groups)){
groups <- levels([email protected][[group.by]])
} else{
if(!all(groups %in% [email protected][[group.by]])){
stop('Some groups not present in [email protected][[group.by]]')
}
}
if(!is.null(selected_split)){
if(!all(selected_split %in% [email protected][[split.by]])){
stop('Some selected_split not present in [email protected][[split.by]] ')
}
}
# number of individual plots to make
n_plots <- length(features) * length(groups)
# colors for groups
if(is.null(group_color_df)){
factor_df <- data.frame(
level = 1:length(levels(seurat_meta[[group.by]])),
group_name = levels(seurat_meta[[group.by]])
)
p <- seurat_meta %>%
ggplot(aes_string(x=1, y=1, color=group.by)) +
geom_point()
g <- ggplot_build(p)
g_df <- g$data[[1]]
group_color_df <- dplyr::select(g_df, c(colour, group)) %>% distinct() %>% arrange(group)
group_color_df$group <- factor_df$group_name
} else if(is.character(group_color_df)){
# check if it's a named character vec:
if(is.null(names(group_color_df))){
stop("color_df must be a dataframe with a group column and a colour column, or color_df can be a named character vector where the values are the colors and the names are the corresponding groups.")
}
group_color_df <- data.frame(
colour = as.character(group_color_df),
group = names(group_color_df)
)
}
# only keep groups that we are using
group_color_df <- subset(group_color_df, group %in% groups)
group_color_df$var <- 1
# color scheme
group_colors <- group_color_df$colour
names(group_colors) <- as.character(group_color_df$group)
# makr color bar in ggplot
if(add_colorbar){
colorbar <- group_color_df %>%
ggplot(aes(x=group, y=var, fill=group)) +
geom_tile() +
scale_fill_manual(values=group_color_df$colour) +
NoLegend() +
theme(
plot.title=element_blank(),
axis.line=element_blank(),
axis.ticks.y=element_blank(),
axis.text.y = element_blank(),
axis.title = element_blank(),
plot.margin=margin(0,0,0,0)
) + RotatedAxis()
}
# initialize progress bar
pb <- utils::txtProgressBar(min = 0, max = n_plots,style = 3, width = 50, char = "=")
patch_list <- list()
i <- 1
for(feature in features){
plot_df <- seurat_meta
if(feature %in% colnames(plot_df)){
plot_df$PlotFeature <- plot_df[[feature]]
}
else{
plot_df$PlotFeature <- GetAssayData(seurat_obj, slot=slot, assay=assay)[feature,]
}
plot_range <- range(plot_df$PlotFeature)
if(add.noise){
noise <- rnorm(n = length(x = plot_df$PlotFeature)) / 100000
plot_df$PlotFeature <- plot_df$PlotFeature + noise
}
# subset selected groups to compare only:
if(!is.null(selected_split)){
plot_df <- plot_df[plot_df[[split.by]] %in% selected_split,]
}
plot_list <- list()
for(cur_group in groups){
cur_df <- plot_df[as.character(plot_df[[group.by]]) == cur_group,]
if(is.null(split.by)){
p <- cur_df %>% ggplot(aes_string(x=group.by, y='PlotFeature', fill=group.by)) +
scale_fill_manual(values=group_colors, na.value = 'grey90')
} else{
p <- cur_df %>% ggplot(aes_string(x=split.by, y='PlotFeature', fill=split.by))
if(!is.null(split_colors)){
p <- p + scale_fill_manual(values=split_colors)
}
}
# add violin
p <- p + geom_violin(
trim=FALSE, adjust=adjust,
scale='width', draw_quantiles = quantiles,
color='black', lwd=0.5
)
if(add_boxplot){
p <- p + geom_boxplot(width=0.1, fill='white', outlier.shape=NA)
}
if(pt.size > 0){
p <- p + ggrastr::rasterise(ggbeeswarm::geom_beeswarm(size=0.5), dpi=raster_dpi)
}
# code for stacked vln plot theme from CellChat:
p <- p + theme(text = element_text(size = 10)) + theme(axis.line = element_line(size=line.size)) +
theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 8), axis.line.x = element_line(colour = 'black', size=line.size),axis.line.y = element_line(colour = 'black', size= line.size))
p <- p + theme(plot.title= element_blank(), # legend.position = "none",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)),
plot.margin = plot.margin ) +
theme(axis.text.y = element_text(size = 8))
p <- p + theme(element_line(size=line.size))
if(length(plot_list) > 0){
p <- p + theme(
legend.position = "none",
axis.line.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y=element_blank(),
axis.text.y=element_blank()
)
}
# y limits:
if(!is.null(plot_ymax)){
ymax <- plot_ymax
} else{
ymax <- ceiling(plot_range[2]);
}
ymin <- floor(plot_range[1])
p <- p + expand_limits(y=c(ymin, ymax)) +
scale_y_continuous(expand=c(0,0), breaks = c(ymax), limits=c(plot_ymin, NA))
# stats:
#if(is.na(comparisons)){
if(!is.null(split.by)){
if(is.null(comparisons)){
p <- p + stat_compare_means(method=stat_method, aes(label = ..p.signif..), label.y=ymax*pval_y_adjust,hide.ns=TRUE)
} else{
print(comparisons)
p <- p + stat_compare_means(
method=stat_method,
label='p.signif',
comparisons=comparisons,
label.y=ymax-(ymax*0.25),
hide.ns=TRUE
# ref.group=ref_group
)
}
}
#}
p <- p + ylab(feature)
# different settings for the last gene:
if(!add_colorbar & feature == features[length(features)]){
p <- p + theme(axis.text.x = element_text(), axis.ticks.x = element_line()) +
RotatedAxis()
}
plot_list[[cur_group]] <- p
# update progress bar
setTxtProgressBar(pb, i)
i <- i+1
}
patch_list[[feature]] <- wrap_plots(plot_list, ncol=length(groups))
}
# close progress bar
close(pb)
if(length(patch_list) == 1){
return(patch_list[[1]])
}
plot_heights <- rep(50 / length(features), length(features))
if(add_colorbar){
plot_heights <- c(plot_heights, 1)
patch_list <- c(patch_list, list(colorbar))
}
wrap_plots(patch_list, ncol=1) + plot_layout(heights=plot_heights, guides='collect')
}
PlotDEGsChromosome <- function(
degs,
gene_table, # matches the gene names in teh seurat object with ensembl ids
EnsDb,
chr_names,
gene_biotypes = c("protein_coding"),
plot_limit = 0.5,
logfc_thresh = 0.25,
raster = TRUE,
raster_dpi = 200,
color_df = NULL,
return_table = FALSE,
high_color = 'blue',
mid_color = 'lightgrey',
low_color = 'yellow'
){
degs$avg_log2FC <- ifelse(
abs(degs$avg_log2FC) > plot_limit,
sign(degs$avg_log2FC) * plot_limit,
degs$avg_log2FC
)
plot_list <- list()
chr_n_genes <- rep(0, length(chr_names))
names(chr_n_genes) <- chr_names
# genes that are differentially expressed in at least one cell type:
print(logfc_thresh)
genes.keep <- unique(degs[abs(degs$avg_log2FC) >= logfc_thresh & degs$p_val_adj < 0.05, 'gene'])
degs <- subset(degs, gene %in% genes.keep)
# make a dummy ggplot to extract color scheme
if(is.null(color_df)){
p <- degs %>%
ggplot(aes(x=avg_log2FC, y=p_val_adj, color=group)) +
geom_point()
g <- ggplot_build(p)
g_df <- g$data[[1]]
color_df <- dplyr::select(g_df, c(colour, group)) %>% distinct() %>% arrange(group)
}
# make the colorbar as its own heatmap
color_df <- subset(color_df, group %in% unique(degs$group))
color_df$var <- 1
colorbar <- color_df %>%
ggplot(aes(y=group, x=var, fill=group)) +
geom_tile() +
scale_fill_manual(values=color_df$colour) +
NoLegend() +
theme(
plot.title=element_blank(),
axis.line=element_blank(),
axis.ticks=element_blank(),
axis.text.x = element_blank(),
axis.title = element_blank(),
plot.margin=margin(0,0,0,0)
)
# output dataframe:
out_degs <- data.frame()
for(cur_chr in chr_names){
print(cur_chr)
# subset the ensembl db based on
gene.coords <- ensembldb::genes(EnsDb, filter = ~ gene_biotype == "protein_coding") %>% subset(seqnames == cur_chr)
# subset by genes in our snRNA data
gene.coords <- gene.coords[gene.coords$gene_id %in% gene_table$gene_id]
ix <- match(gene.coords$gene_id, gene_table$gene_id)
gene.coords$gene_name <- gene_table$gene_name[ix]
# remove entries with duplicate names
dupes <- names(which(table(gene.coords$gene_name) > 1))
dupes <- unlist(lapply(dupes, function(x){which(gene.coords$gene_name == x)[-1]}))
if(!is.null(dupes)){
gene.coords <- gene.coords[-dupes]
}
# subset the DEGs:
cur_degs <- degs %>% subset(gene %in% gene.coords$gene_name)
gene.coords <- gene.coords %>% subset(gene_name %in% cur_degs$gene)
# add chromosome to the table
cur_degs$chr <- cur_chr
if(nrow(cur_degs) == 0){
warning('blank plot')
plot_list[[paste0(cur_g, '_', cur_chr)]] <- ggplot() + theme_void()
next
}
# add order based on genomic coordinates:
gene.coords$order <- 1:length(gene.coords)
chr_n_genes[cur_chr] <- chr_n_genes[cur_chr] + length(gene.coords)
# add the order to the deg table:
cur_degs$order <- match(cur_degs$gene, gene.coords$gene_name)
cur_degs$avg_log2FC <- ifelse(
cur_degs$p_val_adj > 0.05, NA,
cur_degs$avg_log2FC
)
print(dim(cur_degs))
print(length(unique(cur_degs$gene)))
# plot as a heatmap:
p <- cur_degs %>%
ggplot(aes(x = order, y = group, fill = avg_log2FC))
if(raster){
p <- p + ggrastr::rasterise(geom_tile(), dpi=raster_dpi)
} else {
p <- p + geom_tile()
}
p <- p +
scale_fill_steps2(high=high_color, mid=mid_color, low=low_color, limits = c(-plot_limit, plot_limit), n.breaks=5, na.value='lightgrey') +
xlab(cur_chr) +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
plot.margin = margin(0,0,0,0)
)
if(length(plot_list) > -1){
p <- p + theme(
axis.text.y = element_blank(),
)
}
plot_list[[paste0(cur_chr)]] <- p
out_degs <- rbind(out_degs, cur_degs)
}
if(return_table){
return(out_degs)
}
# chr_n_genes <- chr_n_genes / length(cluster_groups)
plot_widths <- 50 * (chr_n_genes / sum(chr_n_genes))
plot_widths <- c(1, plot_widths)
plot_list <- c(list(colorbar), plot_list)
print(length(plot_list))
patch <- wrap_plots(plot_list, ncol=length(chr_names)+1) + plot_layout(widths = plot_widths, guides='collect')
patch
}
PlotEmbedding <- function(
seurat_obj,
group.by,
reduction = 'umap',
split.by = NULL,
plot_under = FALSE,
label = TRUE,
point_size = 1,
legend_point_size = 5,
text_size = 3,
raster = TRUE,
order_points = "shuffle",
x = NULL,
y = NULL,
raster_dpi = 400,
raster_scale = 1,
selected = NULL,
plot_theme = NULL,
color_df = NULL,
plot_ratio=TRUE
){
plot_df <- [email protected]
if(!is.null(x) & !is.null(y)){
plot_df$x <- plot_df[[x]]
plot_df$y <- plot_df[[y]]
} else if(reduction %in% names(seurat_obj@reductions)){
plot_df$x <- seurat_obj@reductions[[reduction]]@cell.embeddings[,1]
plot_df$y <- seurat_obj@reductions[[reduction]]@cell.embeddings[,2]
}
# convert to a factor:
if(!is.factor(plot_df[[group.by]])){
cur_groups <- unique(plot_df[[group.by]])
cur_groups <- cur_groups[order(cur_groups)]
plot_df[[group.by]] <- factor(
as.character(plot_df[[group.by]]),
levels = cur_groups
)
}
# compute coordinates for cluster labels
centroid_df <- data.frame()
if(label){
for(cur_cluster in unique(plot_df[[group.by]])){
cur_meta <- plot_df[plot_df[[group.by]] == cur_cluster,]
df <- data.frame(
cluster = cur_cluster,
x = mean(cur_meta$x),
y = mean(cur_meta$y)
)
centroid_df <- rbind(centroid_df, df)
}
}
# make a dummy ggplot to extract color scheme
if(is.null(color_df)){
factor_df <- data.frame(
level = 1:length(levels(plot_df[[group.by]])),
group_name = levels(plot_df[[group.by]])
)
p <- plot_df %>%
ggplot(aes_string(x='x', y='y', color=group.by)) +
geom_point()
g <- ggplot_build(p)
g_df <- g$data[[1]]
color_df <- dplyr::select(g_df, c(colour, group)) %>% distinct() %>% arrange(group)
color_df$group <- factor_df$group_name
}
# only show selected groups
if(!is.null(selected)){
plot_df[[group.by]][!(plot_df[[group.by]] %in% selected)] <- NA
if(is.factor(plot_df[[group.by]])){
plot_df[[group.by]] <- droplevels(plot_df[[group.by]])
}
if(label){
centroid_df <- subset(centroid_df, cluster %in% selected)
}
}
print(levels(plot_df[[group.by]]))
# shuffle points:
if(order_points == "shuffle"){
plot_df <- plot_df[sample(nrow(plot_df)),]
}
# plot a single embedding
if(is.null(split.by)){
p <- .PlotSingleEmbedding(plot_df, group.by, label, raster, raster_dpi, raster_scale, point_size, legend_point_size, text_size, color_df, centroid_df, plot_theme=plot_theme, plot_ratio=plot_ratio)
} else{
split_groups <- unique(plot_df[[split.by]])
plot_list <- lapply(split_groups, function(cur_split){
print(cur_split)
cur_df <- plot_df[plot_df[[split.by]] == cur_split,]
split_df <- plot_df[plot_df[[split.by]] != cur_split,]
.PlotSingleEmbedding(cur_df, group.by, label, raster, raster_dpi, raster_scale, point_size, legend_point_size, text_size, color_df, centroid_df, split_df, cur_split, plot_theme, plot_under, plot_ratio=plot_ratio)
})
return(plot_list)
}
p
}
.PlotSingleEmbedding <- function(
plot_df,
group.by,
label,
raster,
raster_dpi,
raster_scale,
point_size,
legend_point_size,
text_size,
color_df,
centroid_df,
split_df = NULL,
cur_split = NULL,
plot_theme = NULL,
plot_under=FALSE,
plot_ratio = TRUE
){
p <- plot_df %>%
ggplot(aes_string(x='x', y='y', color=group.by))
# add the under layer for the split plots
if(!is.null(split_df) & plot_under){
# add points
if(raster){
p <- p + ggrastr::rasterise(geom_point(data = split_df, size=point_size/2, color='lightgrey'), dpi=raster_dpi, scale=raster_scale)
} else{
p <- p + geom_point(data = split_df, size=point_size/2, color='lightgrey')
}
}
# add points
if(raster){
p <- p + ggrastr::rasterise(geom_point(size=point_size), dpi=raster_dpi, scale=raster_scale)
} else{
p <- p + geom_point(size=point_size)
}
# add labels
if(label){
p <- p + ggrepel::geom_text_repel(data = centroid_df, label=centroid_df$cluster, color='black', max.overlaps=Inf, size=text_size)
}
# color scheme
plot_colors <- color_df$colour
names(plot_colors) <- as.character(color_df$group)
p <- p + scale_color_manual(values=plot_colors, na.value = 'grey90')
# add title:
if(is.null(cur_split)){
p <- p + ggtitle(group.by) + theme(plot.title=element_text(hjust=0.5))
} else{
p <- p + ggtitle(cur_split) + theme(plot.title=element_text(hjust=0.5))
}
# add theme:
if(!is.null(plot_theme)){
p <- p + plot_theme
}
# fixed coords
if(plot_ratio){
p <- p + coord_equal()
}
# adjust legend point size
p <- p + guides( color = guide_legend(override.aes = list(size=legend_point_size)))
p
}
VisDimPlot <- function(
seurat_obj,
group.by,
sample_col = "Sample",
sample_labels = NULL,
samples_to_plot = NULL,
raster = TRUE,
dpi = 800,
text_size = 8,
ncol=4,
color_df = NULL,
combine=FALSE
){
# check that row and col are in the seurat metadata:
if(!all(c("col", "row") %in% colnames([email protected]))){
stop("Spatial coordinates must be present in [email protected], named row and col.")
}
plot_df <- [email protected]
# check if the group.by is in the meta-data
if(group.by %in% colnames(plot_df)){
if(is.numeric(plot_df[[group.by]])){
stop("Specified group.by a numeric variable. Try plotting with SampleFeaturePlot?")
}
} else{
stop("group.by not found in colnames([email protected]).")
}
# get list of samples to plot:
if(is.null(samples_to_plot)){
samples_to_plot <- unique(plot_df[[sample_col]])
} else{
if(!all(samples_to_plot %in% unique(plot_df[[sample_col]]))){
stop("Some specified samples are not found in [email protected][[sample_col]].")
}
}
# check sample labels:
if(!is.null(sample_labels)){
if(!all(sample_labels %in% colnames([email protected]))){
stop("Some sample_labels are not found in colnames([email protected])")
}
}
# subset plot_df by specified samples:
plot_df <- plot_df[plot_df[[sample_col]] %in% samples_to_plot,]
# subset plot_df by specified samples:
plot_df <- plot_df[plot_df[[sample_col]] %in% samples_to_plot,]
# group by sample_labels
if(!is.null(sample_labels)){
plot_df <- plot_df %>% group_by(across(all_of(sample_labels))) %>%
as.data.frame()
}
# convert to a factor:
if(!is.factor(plot_df[[group.by]])){
cur_groups <- unique(plot_df[[group.by]])
cur_groups <- cur_groups[order(cur_groups)]
plot_df[[group.by]] <- factor(
as.character(plot_df[[group.by]]),
levels = cur_groups
)
}
# make a dummy ggplot to extract color scheme
if(is.null(color_df)){
factor_df <- data.frame(
level = 1:length(levels(plot_df[[group.by]])),
group_name = levels(plot_df[[group.by]])
)
p <- plot_df %>%
ggplot(aes_string(x=1, y=1, color=group.by)) +
geom_point()
g <- ggplot_build(p)
g_df <- g$data[[1]]
color_df <- dplyr::select(g_df, c(colour, group)) %>% distinct() %>% arrange(group)
color_df$group <- factor_df$group_name
} else if(is.character(color_df)){
# check if it's a named character vec:
if(is.null(names(color_df))){
stop("color_df must be a dataframe with a group column and a colour column, or color_df can be a named character vector where the values are the colors and the names are the corresponding groups.")
}
color_df <- data.frame(
colour = as.character(color_df),
group = names(color_df)
)
}
plot_colors <- color_df$colour
names(plot_colors) <- as.character(color_df$group)
# get the ordering of samples to plot:
order_df <- plot_df %>%
group_by(across(all_of(sample_labels))) %>%
arrange(.by_group=TRUE) %>%
dplyr::select(all_of(c(sample_labels, sample_col))) %>%
distinct()
samples_to_plot <- as.character(order_df[[sample_col]])
plot_list <- list()
for(cur_sample in samples_to_plot){
print(cur_sample)
# get data for this sample and get hex vertex coordinates
cur_df <- plot_df[plot_df[[sample_col]] == cur_sample,]
vertices <- make_hex_spots(cur_df, cur_df[[group.by]])
# get sample labels:
if(!is.null(sample_labels)){
cur_labels <- unlist(lapply(sample_labels, function(x){unique(as.character(cur_df[[x]]))}))
plot_label <- paste0(cur_labels, collapse=', ')
} else{
plot_label <- cur_sample
}
# initialize gpgplot
p <- vertices %>%
ggplot(aes_(x=~x.vertex, y=~y.vertex,group=~spot, fill=~fill))
# add all the grey dots with low/zero expression
if(raster){
p <- p + ggrastr::rasterise(geom_polygon(size=0), dpi=dpi)
} else{
p <- p + geom_polygon(size=0)
}
# add extras to plot:
p <- p +
labs(fill = group.by) +
scale_fill_manual(values=plot_colors, na.value = 'grey90') +
coord_equal() +
ggtitle(plot_label) +
theme(
plot.title = element_text(hjust=0.5, face='plain', size=text_size),
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank()
)
plot_list[[cur_sample]] <- p
}
if(length(plot_list) == 1){
return(plot_list[[1]])
} else if(!combine){
return(plot_list)
} else{
patch <- wrap_plots(plot_list, ncol=ncol, widths=1, heights=1) +
plot_layout(guides='collect') +
plot_annotation(title = group.by) & theme(plot.title = element_text(hjust=0.5),)
}
patch
}
SampleFeaturePlot <- function(
seurat_obj,
feature,
sample_col = "Sample",
sample_labels = NULL,
samples_to_plot = NULL,
raster = TRUE,
slot = 'data',
assay = NULL,
dpi = 800,
ncol = 4,
plot_max = 'q100',
plot_min = 'q0',
text_size = 8,
colfunc = colorRampPalette(brewer.pal(11, 'Spectral' )),
rev_colors = FALSE,
combine=TRUE
){
# check that row and col are in the seurat metadata:
if(!all(c("col", "row") %in% colnames([email protected]))){
stop("Spatial coordinates must be present in [email protected], named row and col.")
}
plot_df <- [email protected]
# check if the feature is in the meta-data
if(feature %in% rownames(seurat_obj)){
if(is.null(assay)){assay <- [email protected]}
plot_df$SpatialPlotFeature <- GetAssayData(seurat_obj, slot=slot, assay=assay)[feature,]
} else if(feature %in% colnames(plot_df)){
if(!is.numeric(plot_df[[feature]])){
stop("Specified feature is not numeric. Try plotting with VisDimPlot?")
}
plot_df$SpatialPlotFeature <- plot_df[[feature]]
} else{
stop("feature not found in rownames(seurat_obj) or in colnames([email protected]).")
}
# get list of samples to plot:
if(is.null(samples_to_plot)){
samples_to_plot <- unique(plot_df[[sample_col]])
} else{
if(!all(samples_to_plot %in% unique(plot_df[[sample_col]]))){
stop("Some specified samples are not found in [email protected][[sample_col]].")
}
}
# check sample labels:
if(!is.null(sample_labels)){
if(!all(sample_labels %in% colnames([email protected]))){
stop("Some sample_labels are not found in colnames([email protected])")
}