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run_tests.R
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run_tests.R
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library(tidyr)
library(reshape2)
library(Seurat)
#load package (if not installed)
setwd("~/spatialNetSmooth/")
devtools::load_all()
#Run SpatialNetSmooth on Visium-Data
#Set input and output directories
filepath_data= "~/score_smoothing/data/"
filepath_output = "~/BA/images Visium/"
setwd(filepath_data)
#get coordinates
coord_tab = read.csv('./spatial/tissue_positions_list.csv', header=FALSE)
#get count matrix
filename = 'V1_Breast_Cancer_Block_A_Section_2_filtered_feature_bc_matrix.h5'
#Load object
se = Load10X_Spatial(
'./',
filename = filename
)
#calculate GSEA
seu <- gseaCalc(se)
coordinates <- GetTissueCoordinates(seu, scale="lowres")
#get true labels
library(imager)
file_annot = './spatial/tissue_lowres_image_annotated.png'
im_annot = load.image(file_annot)
liner = function(mat, der = 5, thr=0.8, fill = 5){
mat %>%
deriche(der,order=0,axis="x") %>%
deriche(der,order=0,axis="y") %>%
threshold(thr) %>%
fill(fill)
}
der = B(im_annot) %>% liner(thr = 0.58, fill=0) %>%
bucketfill(x=200, y=200,color="darkorange",sigma=.2) %>%
bucketfill(x=200, y=450, color="darkorange",sigma=.2) %>%
bucketfill(x=350, y=330, color="darkorange",sigma=.2) %>%
bucketfill(x=420, y=300, color="darkorange",sigma=.2) %>%
bucketfill(x=450, y=350, color="darkorange",sigma=.2) %>%
bucketfill(x=450, y=400, color="darkorange",sigma=.2) %>%
bucketfill(x=480, y=420, color="darkorange",sigma=.2) %>% plot()
labeled_gray <- grayscale(der)
plot(labeled_gray)
labeled <- as.matrix(labeled_gray)
labeled <-labeled[,c(nrow(labeled):1)]
labeled <- apply(labeled, 1, rev)
tumor <- NULL
coordinates<- GetTissueCoordinates(se, scale="lowres")
for(i in 1:nrow(coordinates)){
row <- round(coordinates[i,1])
col <- round(coordinates[i,2])
istumor <- labeled[row, col]
if(istumor==0){
istumor <- 0
}else if(istumor > 0.75){
istumor <- 0
}else{
istumor <- 1
}
tumor <- append(tumor, istumor)
}
truth <- tumor
setwd(filepath_output)
write.csv(truth, "truth.csv")
gsea_raw <- [email protected]$gsea_rat_norm
F1_raw <- F1_quant(gsea_raw, truth, gsea_raw)
pdf("gsea_raw.pdf")
plot_quant(gsea_raw, coordinates, truth)
dev.off()
write.csv(gsea_raw, paste(filepath_output, "raw.csv", sep=""), row.names=T)
alphas <- c(0.2, 0.4, 0.6, 0.8)
gsea_spatial <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_spatial[[i]] <- spatial_smooth(seu, a = alphas[i])
}
spatial <- as.data.frame(gsea_spatial)
colnames(spatial) <- c("alpha=0.2", "alpha=0.4", "alpha=0.6", "alpha=0.8")
write.csv(spatial, "spatial_scores.csv", row.names = T)
spatial <- read.csv("spatial_scores.csv", row.names = 1)
F1_spatial <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_spatial[[i]] <- F1_quant(spatial[,i], truth, gsea_raw)
}
gsea_nn <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_nn[[i]] <- nn_smooth(seu, a = alphas[i])
}
nn <- as.data.frame(gsea_nn)
colnames(nn) <- c("alpha=0.2", "alpha=0.4", "alpha=0.6", "alpha=0.8")
write.csv(nn, "nn_scores.csv", row.names = T)
F1_nn <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_nn[[i]] <- F1_quant(nn[,i], truth,gsea_raw)
}
gsea_snn <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_snn[[i]] <- nn_smooth(seu, a = alphas[i], graph="snn")
}
snn <- as.data.frame(gsea_snn)
colnames(snn) <- c("alpha=0.2", "alpha=0.4", "alpha=0.6", "alpha=0.8")
write.csv(snn, "snn_scores.csv", row.names = T)
F1_snn <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_snn[[i]] <- F1_quant(snn[,i], truth, gsea_raw)
}
gsea_nn_spatial <- vector(mode = "list", length = 4)
for (i in 1:4) { # [0.2, 0.2], [0.2,0.4], [0.2, 0.6], ...[0.4, 0.2], [0.4, 0.4]....
item <- vector(mode = "list", length = 4)
for (j in 1:4) {
item[[j]] <- nn_spatial_smooth(seu, a1 = alphas[i],a2 = alphas[j])
}
gsea_nn_spatial[[i]] <- item
}
nn_spatial<- as.data.frame(gsea_nn_spatial)
colnames(nn_spatial) <- c("a1 = 0.2, a2=0.2","a1 = 0.2, a2=0.4", "a1 = 0.2, a2=0.6", "a1 = 0.2, a2=0.8", "a1 = 0.4, a2=0.2", "a1 = 0.4, a2=0.4", "a1 = 0.4, a2=0.6", "a1 = 0.4, a2=0.8", "a1 = 0.6, a2=0.2", "a1 = 0.6, a2=0.4", "a1 = 0.6, a2=0.6", "a1 = 0.6, a2=0.8", "a1 = 0.8, a2=0.2", "a1 = 0.8, a2=0.4", "a1 = 0.8, a2=0.6", "a1 = 0.8, a2=0.8")
write.csv(nn_spatial, "nn_spatial_scores.csv", row.names = T)
F1_nn_spatial <- vector(mode = "list", length = 16)
for (i in 1:16) {
F1_nn_spatial[[i]] <- F1_quant(nn_spatial[,i], truth, gsea_raw)
}
gsea_snn_spatial <- vector(mode = "list", length = 4)
for (i in 1:4) { # [0.2, 0.2], [0.2,0.4], [0.2, 0.6], ...[0.4, 0.2], [0.4, 0.4]....
item <- vector(mode = "list", length = 4)
for (j in 1:4) {
item[[j]] <- nn_spatial_smooth(seu, a1 = alphas[i],a2 = alphas[j], graph="snn")
}
gsea_snn_spatial[[i]] <- item
}
snn_spatial<- as.data.frame(gsea_snn_spatial)
colnames(snn_spatial) <- c("a1 = 0.2, a2=0.2","a1 = 0.2, a2=0.4", "a1 = 0.2, a2=0.6", "a1 = 0.2, a2=0.8", "a1 = 0.4, a2=0.2", "a1 = 0.4, a2=0.4", "a1 = 0.4, a2=0.6", "a1 = 0.4, a2=0.8", "a1 = 0.6, a2=0.2", "a1 = 0.6, a2=0.4", "a1 = 0.6, a2=0.6", "a1 = 0.6, a2=0.8", "a1 = 0.8, a2=0.2", "a1 = 0.8, a2=0.4", "a1 = 0.8, a2=0.6", "a1 = 0.8, a2=0.8")
write.csv(snn_spatial, "snn_spatial_scores.csv", row.names = T)
F1_snn_spatial <- vector(mode = "list", length = 16)
for (i in 1:16) {
F1_snn_spatial[[i]] <- F1_quant(snn_spatial[,i], truth, gsea_raw)
}
gsea_union <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_union[[i]] <- union_smooth(seu, a = alphas[i])
}
union <- as.data.frame(gsea_union)
colnames(union) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
write.csv(union, "union_scores.csv", row.names = T)
F1_union <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_union[[i]] <- F1_quant(union[,i], truth, gsea_raw)
}
gsea_inter <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_inter[[i]] <- inter_smooth(seu, a = alphas[i])
}
inter <- as.data.frame(gsea_inter)
colnames(inter) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
write.csv(inter, "inter_scores.csv", row.names = T)
gsea_inter_snn <- vector(mode = "list", length = 4)
for (i in 1:4) {
gsea_inter_snn[[i]] <- inter_smooth(seu, a = alphas[i], graph="snn")
}
inter_snn <- as.data.frame(gsea_inter_snn)
colnames(inter_snn) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
write.csv(inter_snn, "inter_snn_scores.csv", row.names = T)
F1_inter <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_inter[[i]] <- F1_quant(inter[,i], truth, gsea_raw)
}
F1_inter_snn <- vector(mode = "list", length = 4)
for (i in 1:4) {
F1_inter_snn[[i]] <- F1_quant(inter_snn[,i], truth, gsea_raw)
}
gsea_alpha <- vector(mode = "list", length = 4)
for (i in 1:4) {
item <- vector(mode = "list", length = 4)
for (j in 1:4) {
item[[j]] <- alpha_nn_spatial_smooth(seu, a = alphas[i], alpha = alphas[j])
}
gsea_alpha[[i]] <- item
}
alpha <- as.data.frame(gsea_alpha)
colnames(alpha) <- c("a = 0.2, alpha=0.2", "a = 0.2, alpha=0.4", "a = 0.2, alpha=0.6", "a = 0.2, alpha=0.8", "a = 0.4, alpha=0.2", "a = 0.4, alpha=0.4", "a = 0.4, alpha=0.6", "a = 0.4, alpha=0.8", "a = 0.6, alpha=0.2", "a = 0.6, alpha=0.4", "a = 0.6, alpha=0.6", "a = 0.6, alpha=0.8", "a = 0.8, alpha=0.2", "a = 0.8, alpha=0.4", "a = 0.8, alpha=0.6", "a = 0.8, alpha=0.8")
write.csv(alpha, "alpha_scores.csv", row.names = T)
F1_alpha <- vector(mode = "list", length = 16)
for (i in 1:16) {
F1_alpha[[i]] <- F1_quant(alpha[,i], truth, gsea_raw)
}
nn_df <- as.data.frame(cbind(F1_nn[[1]]$score, F1_nn[[2]]$score, F1_nn[[3]]$score, F1_nn[[4]]$score))
colnames(nn_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
nn_df$Position <- 1:nrow(nn_df)
nn_df$unsmoothed <-F1_raw$score
df_long <- melt(nn_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_nn.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for nn_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
snn_df <- as.data.frame(cbind(F1_snn[[1]]$score, F1_snn[[2]]$score, F1_snn[[3]]$score, F1_snn[[4]]$score))
colnames(snn_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
snn_df$Position <- 1:nrow(snn_df)
snn_df$unsmoothed <-F1_raw$score
df_long <- melt(snn_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_snn.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for snn_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
spatial_df <- as.data.frame(cbind(F1_spatial[[1]]$score, F1_spatial[[2]]$score, F1_spatial[[3]]$score, F1_spatial[[4]]$score))
colnames(spatial_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
spatial_df$Position <- 1:nrow(spatial_df)
spatial_df$unsmoothed <-F1_raw$score
df_long <- melt(spatial_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_spatial.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for spatial_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
union_df <- as.data.frame(cbind(F1_union[[1]]$score, F1_union[[2]]$score, F1_union[[3]]$score, F1_union[[4]]$score))
colnames(union_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
union_df$Position <- 1:nrow(union_df)
union_df$unsmoothed <-F1_raw$score
df_long <- melt(union_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_union.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for union_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
inter_snn_df <- as.data.frame(cbind(F1_inter_snn[[1]]$score, F1_inter_snn[[2]]$score, F1_inter_snn[[3]]$score, F1_inter_snn[[4]]$score))
colnames(inter_snn_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
inter_snn_df$Position <- 1:nrow(inter_snn_df)
inter_snn_df$unsmoothed <-F1_raw$score
df_long <- melt(inter_snn_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_inter_snn.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for inter_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
inter_df <- as.data.frame(cbind(F1_inter[[1]]$score, F1_inter[[2]]$score, F1_inter[[3]]$score, F1_inter[[4]]$score))
colnames(inter_df) <- c("a=0.2", "a=0.4", "a=0.6", "a=0.8")
inter_df$Position <- 1:nrow(inter_df)
inter_df$unsmoothed <-F1_raw$score
df_long <- melt(inter_df, id.vars = "Position", variable.name = "Smoothing Parameter", value.name = "Value")
pdf("F1_inter.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameter`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for inter_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
nn_spatial_df <- as.data.frame(cbind(F1_nn_spatial[[1]]$score, F1_nn_spatial[[2]]$score, F1_nn_spatial[[3]]$score, F1_nn_spatial[[4]]$score, F1_nn_spatial[[5]]$score, F1_nn_spatial[[6]]$score, F1_nn_spatial[[7]]$score, F1_nn_spatial[[8]]$score, F1_nn_spatial[[9]]$score, F1_nn_spatial[[10]]$score, F1_nn_spatial[[11]]$score, F1_nn_spatial[[12]]$score, F1_nn_spatial[[13]]$score, F1_nn_spatial[[14]]$score, F1_nn_spatial[[15]]$score, F1_nn_spatial[[16]]$score))
colnames(nn_spatial_df) <- c("a1 = 0.2, a2=0.2","a1 = 0.2, a2=0.4", "a1 = 0.2, a2=0.6", "a1 = 0.2, a2=0.8", "a1 = 0.4, a2=0.2", "a1 = 0.4, a2=0.4", "a1 = 0.4, a2=0.6", "a1 = 0.4, a2=0.8", "a1 = 0.6, a2=0.2", "a1 = 0.6, a2=0.4", "a1 = 0.6, a2=0.6", "a1 = 0.6, a2=0.8", "a1 = 0.8, a2=0.2", "a1 = 0.8, a2=0.4", "a1 = 0.8, a2=0.6", "a1 = 0.8, a2=0.8")
nn_spatial_df$Position <- 1:nrow(nn_spatial_df)
nn_spatial_df$unsmoothed <-F1_raw$score
df_long <- melt(nn_spatial_df, id.vars = "Position", variable.name = "Smoothing Parameters", value.name = "Value")
pdf("F1_nn_spatial.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameters`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for nn_spatial_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
snn_spatial_df <- as.data.frame(cbind(F1_snn_spatial[[1]]$score, F1_snn_spatial[[2]]$score, F1_snn_spatial[[3]]$score, F1_snn_spatial[[4]]$score, F1_snn_spatial[[5]]$score, F1_snn_spatial[[6]]$score, F1_snn_spatial[[7]]$score, F1_snn_spatial[[8]]$score, F1_snn_spatial[[9]]$score, F1_snn_spatial[[10]]$score, F1_snn_spatial[[11]]$score, F1_snn_spatial[[12]]$score, F1_snn_spatial[[13]]$score, F1_snn_spatial[[14]]$score, F1_snn_spatial[[15]]$score, F1_snn_spatial[[16]]$score))
colnames(snn_spatial_df) <- c("a1 = 0.2, a2=0.2","a1 = 0.2, a2=0.4", "a1 = 0.2, a2=0.6", "a1 = 0.2, a2=0.8", "a1 = 0.4, a2=0.2", "a1 = 0.4, a2=0.4", "a1 = 0.4, a2=0.6", "a1 = 0.4, a2=0.8", "a1 = 0.6, a2=0.2", "a1 = 0.6, a2=0.4", "a1 = 0.6, a2=0.6", "a1 = 0.6, a2=0.8", "a1 = 0.8, a2=0.2", "a1 = 0.8, a2=0.4", "a1 = 0.8, a2=0.6", "a1 = 0.8, a2=0.8")
snn_spatial_df$Position <- 1:nrow(snn_spatial_df)
snn_spatial_df$unsmoothed <-F1_raw$score
df_long <- melt(snn_spatial_df, id.vars = "Position", variable.name = "Smoothing Parameters", value.name = "Value")
pdf("F1_snn_spatial.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Smoothing Parameters`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for snn_spatial_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
alpha_df <- as.data.frame(cbind(F1_alpha[[1]]$score, F1_alpha[[2]]$score, F1_alpha[[3]]$score, F1_alpha[[4]]$score, F1_alpha[[5]]$score, F1_alpha[[6]]$score, F1_alpha[[7]]$score, F1_alpha[[8]]$score, F1_alpha[[9]]$score, F1_alpha[[10]]$score, F1_alpha[[11]]$score, F1_alpha[[12]]$score, F1_alpha[[13]]$score, F1_alpha[[14]]$score, F1_alpha[[15]]$score, F1_alpha[[16]]$score))
colnames(alpha_df) <- c("a = 0.2, alpha=0.2", "a = 0.2, alpha=0.4", "a = 0.2, alpha=0.6", "a = 0.2, alpha=0.8", "a = 0.4, alpha=0.2", "a = 0.4, alpha=0.4", "a = 0.4, alpha=0.6", "a = 0.4, alpha=0.8", "a = 0.6, alpha=0.2", "a = 0.6, alpha=0.4", "a = 0.6, alpha=0.6", "a = 0.6, alpha=0.8", "a = 0.8, alpha=0.2", "a = 0.8, alpha=0.4", "a = 0.8, alpha=0.6", "a = 0.8, alpha=0.8")
alpha_df$Position <- 1:nrow(alpha_df)
alpha_df$unsmoothed <-F1_raw$score
df_long <- melt(alpha_df, id.vars = "Position", variable.name = "Parameters", value.name = "Value")
pdf("F1_alpha.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Parameters`)) +
geom_point() +
theme_minimal() +
labs(title = "F1 scores for alpha_smoothing",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()
best_df <- as.data.frame(cbind(F1_nn[[4]]$score, F1_snn[[4]]$score, F1_inter_snn[[4]]$score, F1_union[[4]]$score, F1_spatial[[4]]$score, F1_nn_spatial[[16]]$score, F1_alpha[[16]]$score, F1_snn_spatial[[16]]$score))
colnames(best_df) <- c("nn", "snn", "intersection", "union", "spatial", "nn_spatial", "alpha", "snn_spatial")
best_df$Position <- 1:nrow(best_df)
best_df$unsmoothed <-F1_raw$score
df_long <- melt(best_df, id.vars = "Position", variable.name = "Method", value.name = "Value")
pdf("F1_best.pdf")
ggplot(df_long, aes(x = Position, y = Value, color = `Method`)) +
geom_point() +
theme_minimal() +
labs(title = "Comparison of different smoothing methods for Visium-Dataset",
x = "Quantile as Threshold",
y = "F1 Score")
dev.off()