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Module2_spatial_coherence_org_zones.R
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library(patchwork)
library(parallel)
#!!! Run Functions section first
# load data ---------------------------------------------------------------
sample_ls <- (read.delim("general/GBM_samples.txt", header = FALSE))$V1
gen_clusters <- as.character(unique(unlist(sapply(c(1:length(sample_ls)), function(i){
mp_assign <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
return(unique(mp_assign$spot_type_meta_new))
}))))
# calculate spatial coherence (Figures 4A-C) --------
rand_num <- 100
sapply(c(1:length(sample_ls)), function(i){
print(sample_ls[i])
# load data
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[i] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
# abundance
programs_comp <- sample_programs_composition(spots_clusters,gen_clusters)
# neighbors tables
neighbors_table <- neighbors_table_func(spots_positions,spots_clusters)
rand_neighbors_table <- lapply(c(1:rand_num), function(i){
new_pos <- sample(spots_positions$V1, length(spots_positions$V1), replace = FALSE)
pos_table <- spots_positions
pos_table$V1 <- new_pos
pos_table$V2 <- spots_positions[new_pos, "V2"]
neighbors_table <- neighbors_table_func(pos_table,spots_clusters)
return(neighbors_table)
})
# spatial coherence
programs_spatial_score <- sapply(sort(gen_clusters), function(cluster){
if (!(cluster %in% spots_clusters$spot_type)) {
prog_score <- NaN
} else {
program_neighbors_table = neighbors_table[row.names(neighbors_table) %in% spots_clusters$barcodes[spots_clusters$spot_type == cluster],]
obs <- obs_program_spatial_score(program_neighbors_table, cluster)
one_spatial_score <- one_val(dim(program_neighbors_table)[1])
zero_spatial_score <- zero_val(rand_neighbors_table, spots_clusters, cluster)
if (obs>one_spatial_score){obs <- one_spatial_score}
if (obs<zero_spatial_score){obs <- zero_spatial_score}
prog_score <- (obs - zero_spatial_score)/(one_spatial_score - zero_spatial_score)
}
return(prog_score)
})
saveRDS(programs_spatial_score, "save_path")
})
# plot
sapply(c(1:length(sample_ls)), function(i){
print(sample_ls[i])
# load data
spots_clusters <- readRDS(paste("MP/mp_assign/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
# abundance
programs_comp <- sample_programs_composition(spots_clusters,gen_clusters)
saveRDS(programs_comp, paste("save_path_abund", sample_ls[i],"_nodes_abund.rds", sep = ""))
})
all_comp <- list.files("save_path_abund")
compositions <- sapply(all_comp, function(comp){
samp_comp <- readRDS(paste("save_path_abund", comp, sep = ""))
return(samp_comp)
})
colnames(compositions) <- as.character(sapply(colnames(compositions), function(x){substr(x, 1, nchar(x)-16)}))
all_spatial_score <- list.files("save_path")
spatial_score <- sapply(all_spatial_score, function(s_score){
samp_scores <- readRDS(paste("save_path", s_score, sep = ""))
return(samp_scores)
})
spatial_score <- as.data.frame(spatial_score)
colnames(spatial_score) <- as.character(sapply(colnames(spatial_score), function(x){substr(x,1, nchar(x)-18)}))
new_score <- ifelse(compositions < 0.05, 0,1) * spatial_score # filtered <0.05
new_score[new_score == 0] <- NA
new_score$metaprogram <- row.names(spatial_score)
spatial_score_filt <- new_score
mal_mp <- c("AC", "NPC", "OPC", "MES", "chromatin.reg", "AC.MES", "metabolism", "inflammatory.resp", "macrophage", "vascular", "oligo", "reactive.astrocyte")
prog_order <- rev(spatial_score_filt$metaprogram[order(apply(spatial_score_filt[,c(1:length(sample_ls))],1, function(x){mean(na.omit(x[x!=0]))}))])
samples_order <- rev(colnames(spatial_score_filt)[c(1:length(sample_ls))][order(apply(spatial_score_filt[spatial_score_filt$metaprogram %in% c("AC", "NPC", "OPC", "MES", "chromatin.reg", "AC.MES", "inflammatory.resp", "macrophage", "vascular","oligo", "reactive.astrocyte"),c(1:length(sample_ls))],2, function(x){mean(na.omit(x[x!=0]))}))])
spatial_long <- melt(setDT(spatial_score_filt), id.vars = "metaprogram", variable.name = "sample", value.name = "spatial_score")
spatial_long <- spatial_long[spatial_long$spatial_score != 0,]
spatial_long$sample <- factor(spatial_long$sample, levels = samples_order)
spatial_long$metaprogram <- factor(spatial_long$metaprogram, levels = prog_order)
mp_spatial_mean <- as.data.frame(apply(new_score[,c(1:26)],1, function(x){mean(na.omit(x))}))
colnames(mp_spatial_mean) <- c("mean_spatial")
mp_spatial_mean$metaprogram <- new_score$metaprogram
mp_spatial_mean$sd <- apply(new_score[,c(1:26)],1, function(x){sd(na.omit(x))})
mp_ord_tmp <- apply(new_score[,c(1:26)],1, function(x){mean(na.omit(x))})
names(mp_ord_tmp) <- new_score$metaprogram
mp_ord <- names(sort(mp_ord_tmp, decreasing = T))
mp_spatial_mean$metaprogram <- factor(mp_spatial_mean$metaprogram, levels = mp_ord)
samp_pal<-met.brewer("Veronese",26)
meta_pal<-c("hypoxia"="#F2B701", "MES"="#F6CF71", "AC.MES"="#66C5CC","AC"="#E68310","OPC"="#f97b72", "NPC"="#7F3C8D","chromatin.reg"="#3969AC", "metabolism"="#EF4868", "neuron"="#4b4b8f","oligo"="#B95FBB", "reactive.astrocyte"="#D4D915", "macrophage"="#80BA5A", "inflammatory.resp"="#11A579","vascular"="#CF1C90")
# plot was not not used in paper
p1_mp <- ggplot(spatial_long, aes(x=factor(metaprogram), y=spatial_score, fill = sample)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5) +
theme_minimal() +
theme(axis.text.x = element_text(size= 12, angle = 45, vjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_text(size=14, face="bold"),
plot.title = element_text(size=14, face="bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank()) +
ylab("spatial cohernece score") +
xlab("metaprogram") +
ggtitle("Spatial Coherence by Cancer Metaprogram") +
scale_fill_manual(values = samp_pal)
# fig 4C
p2_mp <- ggplot(data=mp_spatial_mean, aes(x=metaprogram, y=mean_spatial, group=1)) +
geom_line(color = "grey", size = 2)+
geom_point() +
geom_errorbar(aes(ymin=mean_spatial-sd, ymax=mean_spatial+sd), width=.2,
position=position_dodge(0.05)) + theme_minimal() +
theme(axis.text.x = element_text(size= 12, angle = 45, vjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_text(size=14, face="bold"),
plot.title = element_text(size=14, face="bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank()) +
ylab("mean spatial cohernece score")
#fig 4B
p1_samp <- ggplot(spatial_long[spatial_long$metaprogram %in% c("AC", "NPC", "OPC", "MES", "chromatin.reg", "AC.MES", "inflammatory.resp", "macrophage", "vascular","oligo", "reactive.astrocyte")], aes(x=factor(sample), y=spatial_score, fill = metaprogram)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=1.5) +
theme_minimal() +
theme(axis.text.x = element_text(size= 12, angle = 45, vjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_text(size=14, face="bold"),
plot.title = element_text(size=14, face="bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank()) +
ylab("spatial cohernece score") +
xlab("sample") +
ggtitle("Spatial Coherence by samples") +
scale_fill_manual(values = meta_pal)
#[ plot was not used in paper]
spatial_mean <- as.data.frame(apply(new_score[c(1:3,5:7,10,12,14),c(1:26)],2, function(x){mean(na.omit(x))})) # claculate mean spatial coherence excluding hypoxia and metabolism and normal brain (oligo, neuron and reactive.ast)
colnames(spatial_mean) <- c("mean_spatial")
spatial_mean$sample <- row.names(spatial_mean)
spatial_mean$sd <- apply(new_score[c(1:3,5:7,10,12,14),c(1:26)],2, function(x){sd(na.omit(x))})
samples_ord <- names(sort(apply(new_score[c(1:3,5:7,10,12,14),c(1:26)],2, function(x){mean(na.omit(x))}), decreasing = T))
spatial_mean$sample <- factor(spatial_mean$sample, levels = samples_ord)
p2_samp <- ggplot(data=spatial_mean[spatial_mean$sample,], aes(x=sample, y=mean_spatial, group=1)) +
geom_line(color = "grey", size = 2)+
geom_point() +
geom_errorbar(aes(ymin=mean_spatial-sd, ymax=mean_spatial+sd), width=.2,
position=position_dodge(0.05)) + theme_minimal() +
theme(axis.text.x = element_text(size= 12, angle = 45, vjust = 0.5),
axis.title.x = element_blank(),
axis.title.y = element_text(size=14, face="bold"),
plot.title = element_text(size=14, face="bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank()) +
ylab("mean spatial cohernece score")
# calculate spatial coherence by window to define structured vs organized spots (Figures 4D-E) (Alt. skip and use saved results in next section) ----------------------------------------------------------
win_size <- 5 # run for c(5,8,11)
rand_num <- 100
samples_num <- c(1:length(sample_ls))
all_scores <- mclapply(samples_num, all_scores_fx, mc.cores = 26)
all_scores_fx <- function(i){
#print(sample_ls[i])
# load data
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[i] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
# neighbors tables
all_win_neighbors_table <- neighbors_table_funcV2(spots_positions,spots_clusters)
neighbors_table <- neighbors_table_func(spots_positions,spots_clusters)
# spatial coherence by window
all_spots <- spots_clusters$barcodes
spatial_score_win <- sapply(all_spots, function(spot){
win_spots <- c(spot)
sapply(c(1:win_size), function(j){
win_spots <<- unique(c(win_spots,unique(na.omit(as.character(all_win_neighbors_table[win_spots,])))))
win_spots <<- win_spots[win_spots != "NaN"]
})
win_rand_neighbors_table <- lapply(c(1:rand_num), function(k){
win_spots_positions <- spots_positions[spots_positions$V1 %in% win_spots,]
new_pos <- sample(win_spots_positions$V1, length(win_spots_positions$V1), replace = FALSE)
pos_table <- win_spots_positions
row.names(pos_table) <- new_pos
pos_table$V1 <- new_pos
pos_table$V2 <- win_spots_positions[new_pos, "V2"]
neighbors_table <- win_prox_neighbors_table_func(pos_table,spots_clusters[row.names(pos_table),])
return(neighbors_table)
})
win_abund <- sample_programs_composition(spots_clusters[spots_clusters$barcodes %in% win_spots,], gen_clusters)
programs_spatial_score <- lapply(sort(gen_clusters), function(cluster){
if (!(cluster %in% spots_clusters$spot_type[spots_clusters$barcodes %in% win_spots]) |
length(spots_clusters$barcodes[spots_clusters$spot_type == cluster & spots_clusters$barcodes %in% win_spots]) < 10) {
prog_score <- NaN
return(prog_score)
} else {
program_neighbors_table = neighbors_table[row.names(neighbors_table) %in% spots_clusters$barcodes[spots_clusters$spot_type == cluster & spots_clusters$barcodes %in% win_spots],]
obs <- obs_program_spatial_score(program_neighbors_table, cluster)
one_spatial_score <- one_val(dim(program_neighbors_table)[1])
zero_spatial_score <- zero_val(win_rand_neighbors_table, spots_clusters[spots_clusters$barcodes %in% win_spots,], cluster)
if (win_abund[cluster] >= 0.6) {
if (obs>one_spatial_score){obs <- one_spatial_score}
return(obs/one_spatial_score)
} else {
if (obs>one_spatial_score){obs <- one_spatial_score}
if (obs<zero_spatial_score){obs <- zero_spatial_score}
prog_score <- (obs - zero_spatial_score)/(one_spatial_score - zero_spatial_score)
return(prog_score)
}
}
})
return(mean(na.omit(unlist(programs_spatial_score))))
})
return(spatial_score_win)
}
# define dis-organized zones ------------------------------------------------------------
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win5v3.rds")
hist(unlist(all_scores), breaks = 100, main = "radius 5: spatial coherence socres", xlab = "spatial coherence score")
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones5 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] <= th1, "dis", "other")
print(table(set_zones))
return(set_zones)
})
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win8v3.rds")
hist(unlist(all_scores)[unlist(all_scores) > 0 & unlist(all_scores) < 1], breaks = 100, main = "radius 8: spatial coherence socres", xlab = "spatial coherence score")
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones10 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] <= th1, "dis", "other")
print(table(set_zones))
return(set_zones)
})
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win11v3.rds")
hist(unlist(all_scores)[unlist(all_scores) > 0 & unlist(all_scores) < 1], breaks = 100, main = "radius 11: spatial coherence socres", xlab = "spatial coherence score")
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones15 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] <= th1, "dis", "other")
print(table(set_zones))
return(set_zones)
})
zones_intersect <- sapply(c(1:26), function(x){
inter1 <- intersect(names(all_zones5[[x]])[all_zones5[[x]] == "dis"],names(all_zones10[[x]])[all_zones10[[x]] == "dis"])
inter2 <- intersect(inter1,names(all_zones15[[x]])[all_zones15[[x]] == "dis"])
return(inter2)
})
all_zones <- sapply(c(1:26),function(i){
set_zones <- ifelse(names(all_scores[[i]]) %in% zones_intersect[[i]],"dis","other")
names(set_zones) <- names(all_scores[[i]])
return(set_zones)
})
win_size <- 4
all_smooth <- sapply(c(1:26),function(i){
print(sample_ls[i])
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[i] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
neighbors_table <- prox_neighbors_table_func(spots_positions,spots_clusters)
all_spots <- spots_clusters$barcodes
smoothing <- sapply(all_spots, function(spot){
win_spots <- c(spot)
sapply(c(1:win_size), function(i){
win_spots <<- unique(c(win_spots,unique(na.omit(as.character(neighbors_table[win_spots,])))))
})
win_zones <- all_zones[[i]][names(all_zones[[i]]) %in% win_spots]
if (length(win_zones[win_zones == "dis"])/length(win_zones) >= 0.5) {return("dis")}
else {return("other")}
})
return(smoothing)
})
# define struct zones -----------------------------------------------------
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win5v3.rds")
hist(unlist(all_scores), breaks = 100)
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones5 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] >= th1, "struct","other")
print(table(set_zones))
return(set_zones)
})
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win8v3.rds")
hist(unlist(all_scores)[unlist(all_scores) > 0 & unlist(all_scores) < 1], breaks = 100)
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones10 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] >= th1, "struct", "other")
print(table(set_zones))
return(set_zones)
})
all_scores <- readRDS("Spatial_coh_zones/sc_windows/spatial_win11v3.rds")
hist(unlist(all_scores)[unlist(all_scores) > 0 & unlist(all_scores) < 1], breaks = 100)
th1 <- as.numeric(quantile(na.omit(unlist(all_scores)),probs = (seq(0,1,0.1)))[5])
all_zones15 <- sapply(c(1:length(sample_ls)), function(i){
set_zones <- ifelse(all_scores[[i]] >= th1, "struct", "other")
print(table(set_zones))
return(set_zones)
})
zones_intersect <- sapply(c(1:26), function(x){
inter1 <- intersect(names(all_zones5[[x]])[all_zones5[[x]] == "struct"],names(all_zones10[[x]])[all_zones10[[x]] == "struct"])
inter2 <- intersect(inter1,names(all_zones15[[x]])[all_zones15[[x]] == "struct"])
return(inter2)
})
all_zones <- sapply(c(1:26),function(i){
set_zones <- ifelse(names(all_scores[[i]]) %in% zones_intersect[[i]],"struct","other")
names(set_zones) <- names(all_scores[[i]])
return(set_zones)
})
win_size <- 4
all_smooth_struct <- sapply(c(1:26),function(i){
print(sample_ls[i])
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[i] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
neighbors_table <- prox_neighbors_table_func(spots_positions,spots_clusters)
all_spots <- spots_clusters$barcodes
smoothing <- sapply(all_spots, function(spot){
win_spots <- c(spot)
sapply(c(1:win_size), function(i){
win_spots <<- unique(c(win_spots,unique(na.omit(as.character(neighbors_table[win_spots,])))))
})
win_zones <- all_zones[[i]][names(all_zones[[i]]) %in% win_spots]
if (length(win_zones[win_zones == "struct"])/length(win_zones) >= 0.5) {return("struct")}
else {return("other")}
})
return(smoothing)
})
# final zones -------------------------------------------------------------
all_zones <- sapply(c(1:26),function(i){
sapply(names(all_smooth[[i]]), function(s){
if (all_smooth[[i]][s] == "dis" & all_smooth_struct[[i]][s] == "struct") {
return("Intermediate")
} else if (all_smooth[[i]][s] == "dis") {
return("dis")
} else if (all_smooth_struct[[i]][s] == "struct") {
return("struct")
} else {
return("Intermediate")
}
})
})
names(all_zones) <- sample_ls
is_small <- sapply(all_zones, function(x){
tx <- table(x)
old_clusters <- names(tx)
add_clusters <- c("dis","Intermediate", "struct")[!c("dis","Intermediate", "struct") %in% old_clusters]
if (length(add_clusters) > 0) {
tx <- c(as.numeric(tx),0)
names(tx) <- c(old_clusters, add_clusters)
}
tx <- tx[sort(names(tx))]
tx10 <- sum(tx)*0.12
print(tx)
return(c(tx[1]<tx10, tx[2]<tx10,tx[3]<tx10))
})
colnames(is_small) <- sample_ls
dis2use <- c(1:26)[!is_small[1,]]
sturct2use <- c(1:26)[!is_small[3,]]
spatial_score_zones <- lapply(c(1:length(sample_ls)), function(i){
# load data
print(sample_ls[i])
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[i] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[i], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
zone_score <- lapply(c("dis", "struct"), function(z){
zspots <- na.omit(names(all_zones[[i]])[all_zones[[i]] == z]) # checkk!!!!!!!!!!!
if (length(zspots) == 0) {
return(NA)
} else {
# abundance
programs_comp <- sample_programs_composition(spots_clusters[spots_clusters$barcodes %in% zspots,],gen_clusters)
# neighbors tables
neighbors_table <- neighbors_table_func(spots_positions,spots_clusters)
zone_neighbors_table <- neighbors_table[zspots,]
win_rand_neighbors_table <- lapply(c(1:rand_num), function(i){
zspots_positions <- spots_positions[spots_positions$V1 %in% zspots,]
new_pos <- sample(zspots_positions$V1, length(zspots_positions$V1), replace = FALSE)
pos_table <- zspots_positions
row.names(pos_table) <- new_pos
pos_table$V1 <- new_pos
pos_table$V2 <- zspots_positions[new_pos, "V2"]
neighbors_table <- win_prox_neighbors_table_func(pos_table,spots_clusters[spots_clusters$barcodes %in% zspots,])
return(neighbors_table)
})
# spatial coherence
programs_spatial_score <- sapply(sort(gen_clusters), function(cluster){
if (!(cluster %in% spots_clusters$spot_type[spots_clusters$barcodes %in% zspots]) |
length(spots_clusters$barcodes[spots_clusters$spot_type == cluster & spots_clusters$barcodes %in% zspots]) < 3) {
prog_score <- NaN
} else {
program_neighbors_table = neighbors_table[row.names(neighbors_table) %in% spots_clusters$barcodes[spots_clusters$spot_type == cluster & spots_clusters$barcodes %in% zspots],]
obs <- obs_program_spatial_score(program_neighbors_table, cluster)
one_spatial_score <- one_val(dim(program_neighbors_table)[1])
zero_spatial_score <- zero_val(win_rand_neighbors_table, spots_clusters[spots_clusters$barcodes %in% zspots,], cluster)
if (obs>one_spatial_score){obs <- one_spatial_score}
if (obs<zero_spatial_score){obs <- zero_spatial_score}
prog_score <- (obs - zero_spatial_score)/(one_spatial_score - zero_spatial_score)
}
return(prog_score)
})
return(list(programs_comp,programs_spatial_score))
}
})
names(zone_score) <- c("dis", "struct")
return(zone_score)
})
# plot Relationship between spot purity and spatial coherence -------------
purity_score_scaled <- readRDS("CNA/mal_lev.rds")
all_dis_purity <- sapply(c(1:26), function(i){
dis_purity <- purity_score_scaled[[i]][all_zones[[i]] == "dis"]
return(dis_purity)
})
hist(unlist(all_dis_purity))
all_st_purity <- sapply(c(1:26), function(i){
st_purity <- purity_score_scaled[[i]][all_zones[[i]] == "struct"]
return(st_purity)
})
hist(unlist(all_st_purity))
zones_class_fin <- sapply(c(1:26), function(i){
print(sample_ls[[i]])
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[[i]], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
v2 <- sapply(c(1:length(all_zones[[i]])), function(s){
if(all_zones[[i]][s] == "dis" & purity_score_scaled[[i]][names(all_zones[[i]][s])] < 0.5) {
return("dis_norm")
} else if (all_zones[[i]][s] == "dis" & purity_score_scaled[[i]][names(all_zones[[i]][s])] >= 0.5) {
return("dis_mal")
} else if (all_zones[[i]][s] == "struct" & purity_score_scaled[[i]][names(all_zones[[i]][s])] < 0.4) {
return("st_norm")
} else if (all_zones[[i]][s] == "struct" & purity_score_scaled[[i]][names(all_zones[[i]][s])] >= 0.4) {
return("st_mal")
} else {
return(all_zones[[i]][s])
}
})
names(v2) <- names(all_zones[[i]])
return(v2)
})
names(zones_class_fin) <- sample_ls
zone.colors <- c(structured_mal = "#cf5e4e",structured_norm = "#a82203", disorganized_mal = "#208cc0", disorganized_norm = "#003967",intermediate ="#f1af3a")
sapply(c(1:26), function(i){
print(sample_ls[[i]])
spots_positions <- read.csv(paste("general/GBM_data/", sample_ls[[i]] , "/outs/spatial/tissue_positions_list.csv", sep = ""), header = FALSE, stringsAsFactors = FALSE)
row.names(spots_positions) <- spots_positions$V1
spots_clusters <- readRDS(paste("MP/mp_assign_124/", sample_ls[[i]], ".rds", sep = ""))
spots_clusters <- na.omit(spots_clusters)
colnames(spots_clusters) <- c("barcodes", "spot_type")
row.names(spots_clusters)<- spots_clusters$barcodes
spots_filt = spots_positions[spots_positions$V1 %in% spots_clusters$barcodes,]
row2plot1 <- spots_clusters$barcodes[spots_clusters$barcodes %in% names(zones_class_fin[[i]])[zones_class_fin[[i]] == "st_mal"]]
row2plot2 <- spots_clusters$barcodes[spots_clusters$barcodes %in% names(zones_class_fin[[i]])[zones_class_fin[[i]] == "st_norm"]]
row2plot3 <- spots_clusters$barcodes[spots_clusters$barcodes %in% names(zones_class_fin[[i]])[zones_class_fin[[i]] == "dis_mal"]]
row2plot4 <- spots_clusters$barcodes[spots_clusters$barcodes %in% names(zones_class_fin[[i]])[zones_class_fin[[i]] == "dis_norm"]]
spots_filt$plot <- factor(ifelse(spots_filt$V1 %in% row2plot1, "structured_mal",
ifelse(spots_filt$V1 %in% row2plot2, "structured_norm",
ifelse(spots_filt$V1 %in% row2plot3, "disorganized_mal",
ifelse(spots_filt$V1 %in% row2plot4, "disorganized_norm","intermediate")))), levels = c("disorganized_mal","disorganized_norm", "structured_mal", "structured_norm","intermediate"))
spots_filt$V3_ops = -(spots_filt$V3)
gg = ggplot(spots_filt, aes(x=V4, y=V3_ops)) +
geom_point(aes(col=plot), size=2) +
labs(title=paste(sample_ls[[i]], "spatial coherence zones"), y="pos y", x="pos x") +
scale_color_manual(values = zone.colors, name = "zone") +
theme_void()
print(gg)
})
zones_cat <- c("dis_mal","dis_norm","Intermediate","st_mal","st_norm")
zone_abund <- sapply(zones_class_fin, function(z){
spots_zone <- as.data.frame(z)
spots_zone <- na.omit(spots_zone)
spots_zone$barcodes <- row.names(spots_zone)
colnames(spots_zone) <- c("spot_type","barcodes")
# abundance
zones_comp <- sample_programs_composition(spots_zone,zones_cat)
return(zones_comp)
})
######## Functions --------------------------------------------------------------
sample_programs_composition <- function(spots_clusters, gen_clusters){
composition <- table(spots_clusters$spot_type)
old_clusters <- names(composition)
add_clusters <- gen_clusters[!gen_clusters %in% old_clusters]
sapply(add_clusters,function(clust){
composition <<- c(composition, clust = 0)
})
names(composition) <- c(old_clusters, add_clusters)
final_composition <- composition[sort(names(composition))]/sum(composition)
return(final_composition)
}
obs_program_spatial_score <- function(program_neighbors, cluster){
cluster_neighbors_bin <- ifelse(program_neighbors == cluster, 1, 0)
if(is.null(dim(program_neighbors))){
cluster_neighbors_sum <- sum(cluster_neighbors_bin)
} else {
cluster_neighbors_sum <- apply(cluster_neighbors_bin,1,function(rx){sum(na.omit(rx))})
}
obs <- mean(cluster_neighbors_sum)
return(obs)
}
one_val <- function(spots_num){
a <- sqrt((4*spots_num)/(6*sqrt(3)))
oneval <- (6*spots_num-12*a-6)/spots_num
return(oneval)
}
zero_val <- function(rand_table, spots_clusters, cluster){
all_zeroval <- sapply(rand_table, function(neighbors_rand_table){
program_rand_neighbors_table = neighbors_rand_table[row.names(neighbors_rand_table) %in% spots_clusters$barcodes[spots_clusters$spot_type == cluster],]
rand_obs <- obs_program_spatial_score(program_rand_neighbors_table, cluster)
return(rand_obs)
})
zeroval <- mean(all_zeroval)
return(zeroval)
}
calc_adj_mat <- function(neighbored_cluster, spots_clusters, cluster_neighbors_table){
if (!(neighbored_cluster %in% spots_clusters$spot_type)) {
return(0)
} else {
cluster_neighbors_bin <- ifelse(cluster_neighbors_table == neighbored_cluster, 1, 0)
if (is.null(dim(cluster_neighbors_bin))){
cluster_neighbors_sum <- sum(na.omit(cluster_neighbors_bin))
} else {
cluster_neighbors_sum <- sum(apply(cluster_neighbors_bin,1,function(x){sum(na.omit(x))}))
}
return(cluster_neighbors_sum)
}
}
prog_connectivity_score <- function(program_neighbors, cluster){
cluster_neighbors_bin <- ifelse(program_neighbors == cluster, 0, 1)
if(is.null(dim(program_neighbors))){
prog_connect <- 1
} else {
prog_connect <- length(which(apply(cluster_neighbors_bin,1,function(x){sum(na.omit(x))}) >0))
}
return(prog_connect)
}
neighbors_table_func <- function(spots_positions,spots_clusters){
neighbors_table <- sapply(spots_clusters$barcodes, function(spot){
spots_row = spots_positions[spots_positions$V1 == spot, 3]
spots_col = spots_positions[spots_positions$V1 == spot, 4]
if (spots_col == 0 | spots_row == 0) {
c1 = NaN
} else {
n1 = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col - 1]
if (spots_positions$V2[spots_positions$V1 == n1] == 0 | !(n1 %in% spots_clusters$barcodes)){
c1 = NaN
} else {
c1 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n1])
}
}
if (spots_col == 127 | spots_row == 0) {
c2 = NaN
} else {
n2 = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col + 1]
if (spots_positions$V2[spots_positions$V1 == n2] == 0 | !(n2 %in% spots_clusters$barcodes)){
c2 = NaN
} else {
c2 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n2])
}
}
if (spots_col == 0 | spots_col == 1) {
c3 = NaN
} else {
n3 = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col - 2]
if (spots_positions$V2[spots_positions$V1 == n3] == 0 | !(n3 %in% spots_clusters$barcodes)){
c3 = NaN
} else {
c3 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n3])
}
}
if (spots_col == 126 | spots_col == 127) {
c4 = NaN
} else {
n4 = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col + 2]
if (spots_positions$V2[spots_positions$V1 == n4] == 0 | !(n4 %in% spots_clusters$barcodes)){
c4 = NaN
} else {
c4 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n4])
}
}
if (spots_col == 0 | spots_row == 77) {
c5 = NaN
} else {
n5 = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col - 1]
if (spots_positions$V2[spots_positions$V1 == n5] == 0 | !(n5 %in% spots_clusters$barcodes)){
c5 = NaN
} else {
c5 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n5])
}
}
if (spots_col == 127 | spots_row == 77) {
c6 = NaN
} else {
n6 = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col + 1]
if (spots_positions$V2[spots_positions$V1 == n6] == 0 | !(n6 %in% spots_clusters$barcodes)){
c6 = NaN
} else {
c6 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n6])
}
}
return(c(c1,c2,c3,c4,c5,c6))
})
neighbors_table = t(neighbors_table)
row.names(neighbors_table) = spots_clusters$barcodes
return(neighbors_table)
}
neighbors_table_funcV2 <- function(spots_positions,spots_clusters){
neighbors_table <- sapply(spots_clusters$barcodes, function(spot){
spots_row = spots_positions[spots_positions$V1 == spot, 3]
spots_col = spots_positions[spots_positions$V1 == spot, 4]
if (spots_col == 0 | spots_row == 0) {
c1 = NaN
} else {
n1 = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col - 1]
if (spots_positions$V2[spots_positions$V1 == n1] == 0 | !(n1 %in% spots_clusters$barcodes)){
c1 = NaN
} else {
c1 = as.character(n1)
}
}
if (spots_col == 127 | spots_row == 0) {
c2 = NaN
} else {
n2 = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col + 1]
if (spots_positions$V2[spots_positions$V1 == n2] == 0 | !(n2 %in% spots_clusters$barcodes)){
c2 = NaN
} else {
c2 = as.character(n2)
}
}
if (spots_col == 0 | spots_col == 1) {
c3 = NaN
} else {
n3 = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col - 2]
if (spots_positions$V2[spots_positions$V1 == n3] == 0 | !(n3 %in% spots_clusters$barcodes)){
c3 = NaN
} else {
c3 = as.character(n3)
}
}
if (spots_col == 126 | spots_col == 127) {
c4 = NaN
} else {
n4 = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col + 2]
if (spots_positions$V2[spots_positions$V1 == n4] == 0 | !(n4 %in% spots_clusters$barcodes)){
c4 = NaN
} else {
c4 = as.character(n4)
}
}
if (spots_col == 0 | spots_row == 77) {
c5 = NaN
} else {
n5 = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col - 1]
if (spots_positions$V2[spots_positions$V1 == n5] == 0 | !(n5 %in% spots_clusters$barcodes)){
c5 = NaN
} else {
c5 = as.character(n5)
}
}
if (spots_col == 127 | spots_row == 77) {
c6 = NaN
} else {
n6 = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col + 1]
if (spots_positions$V2[spots_positions$V1 == n6] == 0 | !(n6 %in% spots_clusters$barcodes)){
c6 = NaN
} else {
c6 = as.character(n6)
}
}
return(c(c1,c2,c3,c4,c5,c6))
})
neighbors_table = t(neighbors_table)
row.names(neighbors_table) = spots_clusters$barcodes
return(neighbors_table)
}
win_prox_neighbors_table_func <- function(spots_positions,spots_clusters){
neighbors_table <- sapply(spots_clusters$barcodes, function(spot){
spots_row = spots_positions[spots_positions$V1 == spot, 3]
spots_col = spots_positions[spots_positions$V1 == spot, 4]
if (spots_col == 0 | spots_row == 0) {
n1 = NA
} else {
n1_temp = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col - 1]
if (length(n1_temp) == 0) {
n1 = NA
} else if (spots_positions$V2[spots_positions$V1 == n1_temp] == 0 | !(n1_temp %in% spots_clusters$barcodes)){
n1 = NA
} else {
n1 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n1_temp])
}
}
if (spots_col == 127 | spots_row == 0) {
n2 = NA
} else {
n2_temp = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col + 1]
if (length(n2_temp) == 0) {
n2 = NA
} else if (spots_positions$V2[spots_positions$V1 == n2_temp] == 0 | !(n2_temp %in% spots_clusters$barcodes)){
n2 = NA
} else {
n2 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n2_temp])
}
}
if (spots_col == 0 | spots_col == 1) {
n3 = NA
} else {
n3_temp = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col - 2]
if (length(n3_temp) == 0) {
n3 = NA
} else if (spots_positions$V2[spots_positions$V1 == n3_temp] == 0 | !(n3_temp %in% spots_clusters$barcodes)){
n3 = NA
} else {
n3 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n3_temp])
}
}
if (spots_col == 126 | spots_col == 127) {
n4 = NA
} else {
n4_temp = spots_positions$V1[spots_positions$V3 == spots_row & spots_positions$V4 == spots_col + 2]
if (length(n4_temp) == 0) {
n4 = NA
} else if (spots_positions$V2[spots_positions$V1 == n4_temp] == 0 | !(n4_temp %in% spots_clusters$barcodes)){
n4 = NA
} else {
n4 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n4_temp])
}
}
if (spots_col == 0 | spots_row == 77) {
n5 = NA
} else {
n5_temp = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col - 1]
if (length(n5_temp) == 0) {
n5 = NA
} else if (spots_positions$V2[spots_positions$V1 == n5_temp] == 0 | !(n5_temp %in% spots_clusters$barcodes)){
n5 = NA
} else {
n5 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n5_temp])
}
}
if (spots_col == 127 | spots_row == 77) {
n6 = NA
} else {
n6_temp = spots_positions$V1[spots_positions$V3 == spots_row + 1 & spots_positions$V4 == spots_col + 1]
if (length(n6_temp) == 0) {
n6 = NA
} else if (spots_positions$V2[spots_positions$V1 == n6_temp] == 0 | !(n6_temp %in% spots_clusters$barcodes)){
n6 = NA
} else {
n6 = as.character(spots_clusters$spot_type[spots_clusters$barcodes == n6_temp])
}
}
return(c(n1,n2,n3,n4,n5,n6))
})
neighbors_table = t(neighbors_table)
row.names(neighbors_table) = spots_clusters$barcodes
return(neighbors_table)
}
prox_neighbors_table_func <- function(spots_positions,spots_clusters){
neighbors_table <- sapply(spots_clusters$barcodes, function(spot){
spots_row = spots_positions[spots_positions$V1 == spot, 3]
spots_col = spots_positions[spots_positions$V1 == spot, 4]
if (spots_col == 0 | spots_row == 0) {
n1 = NA
} else {
n1_temp = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col - 1]
if (spots_positions$V2[spots_positions$V1 == n1_temp] == 0 | !(n1_temp %in% spots_clusters$barcodes)){
n1 = NA
} else {
n1 = n1_temp
}
}
if (spots_col == 127 | spots_row == 0) {
n2 = NA
} else {
n2_temp = spots_positions$V1[spots_positions$V3 == spots_row - 1 & spots_positions$V4 == spots_col + 1]
if (spots_positions$V2[spots_positions$V1 == n2_temp] == 0 | !(n2_temp %in% spots_clusters$barcodes)){
n2 = NA
} else {
n2 = n2_temp
}
}
if (spots_col == 0 | spots_col == 1) {
n3 = NA
} else {