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discrimination_curves.R
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discrimination_curves.R
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#:--------------------------------------------------------
#
# In this file we check AUROC for the MODY models
#
#:--------------------------------------------------------
# load libraries
library(tidyverse)
library(nimble)
library(pROC)
library(PRROC)
# load functions needed
source("data/create_data.R")
source("new_data_predictions/prediction_functions.R")
# load files required
predictions_dataset.UNITED_type1_all_genes_no_T_full <- readRDS("model_predictions/predictions_dataset.UNITED_type1_all_genes_no_T_full.rds")
predictions_dataset.UNITED_type1_all_genes_with_T_full <- readRDS("model_predictions/predictions_dataset.UNITED_type1_all_genes_with_T_full.rds")
predictions_dataset.UNITED_type2_all_genes_new_full <- readRDS("model_predictions/predictions_dataset.UNITED_type2_all_genes_new_full.rds")
predictions_dataset.UNITED_type1_all_genes_no_T <- readRDS("model_predictions/predictions_dataset.UNITED_type1_all_genes_no_T.rds")
predictions_dataset.UNITED_type1_all_genes_with_T <- readRDS("model_predictions/predictions_dataset.UNITED_type1_all_genes_with_T.rds")
predictions_dataset.UNITED_type2_all_genes_new <- readRDS("model_predictions/predictions_dataset.UNITED_type2_all_genes_new.rds")
# load datasets
## Load population representative dataset
dataset.UNITED_type1_all_genes <- create_data(dataset = "united t1d", commonmody = FALSE) %>%
## if MODY testing missing, change to 0
mutate(M = ifelse(is.na(M), 0, M))
dataset.UNITED_type2_all_genes <- create_data(dataset = "united t2d", commonmody = FALSE)
#:------------------------------------------------------------
plot_prob_density <- patchwork::wrap_plots(
# T1D models
patchwork::wrap_plots(
# Clinical features
patchwork::wrap_plots(
# Density
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_no_T$prob) %>%
filter(M == 0) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_density(aes(x = prob), fill = "grey") +
coord_cartesian(xlim =c(0, 1)) +
scale_x_continuous(labels = scales::percent) +
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
ylab("Non-MODY (n=1164)"),
#point
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_no_T$prob) %>%
filter(M == 1) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_point(aes(x = prob, y=0), position = position_jitter(height = 0.1, seed = 20)) +
coord_cartesian(xlim =c(0, 1), ylim = c(-0.15, 0.15)) +
scale_x_continuous(labels = scales::percent) +
theme_classic() +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
#axis.title.y = element_blank()
) +
ylab("MODY \n cases \n (n=10)") +
xlab("Model probabilities"),
ncol = 1, nrow = 2, heights = c(4,1)
),
# Biomarkers
patchwork::wrap_plots(
# Density
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_with_T$prob) %>%
filter(M == 0) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_density(aes(x = prob), fill = "grey") +
coord_cartesian(xlim =c(0, 1)) +
scale_x_continuous(labels = scales::percent) +
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
ylab("Non-MODY (n=1164)"),
#point
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_with_T$prob) %>%
filter(M == 1) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_point(aes(x = prob, y=0), position = position_jitter(height = 0.1, seed = 20)) +
coord_cartesian(xlim =c(0, 1), ylim = c(-0.15, 0.15)) +
scale_x_continuous(labels = scales::percent) +
theme_classic() +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
#axis.title.y = element_blank()
) +
ylab("MODY \n cases \n (n=10)") +
xlab("Model probabilities"),
ncol = 1, nrow = 2, heights = c(4,1)
),
nrow = 1, ncol = 2
),
# T2D models
patchwork::free(patchwork::wrap_plots(
# Density
dataset.UNITED_type2_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type2_all_genes_new$prob) %>%
filter(M == 0) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_density(aes(x = prob), fill = "grey") +
coord_cartesian(xlim =c(0, 1)) +
scale_x_continuous(labels = scales::percent) +
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
ylab("Non-MODY (n=102)"),
#point
dataset.UNITED_type2_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type2_all_genes_new$prob) %>%
filter(M == 1) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_point(aes(x = prob, y=0), position = position_jitter(height = 0.1, seed = 20)) +
coord_cartesian(xlim =c(0, 1), ylim = c(-0.15, 0.15)) +
scale_x_continuous(labels = scales::percent) +
theme_classic() +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
#axis.title.y = element_blank()
) +
ylab("MODY \n cases \n (n=23)") +
xlab("Model probabilities"),
ncol = 1, nrow = 2, heights = c(4,1)
)
),
nrow = 2, ncol = 1
) + patchwork::plot_annotation(tag_levels = list(c("A.1", "", "A.2", "", "B", ""))) +
patchwork::plot_layout(
design = "
AAAA
#BB#
"
) &
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
strip.text = element_text(size = 11)
)
# Making plots
pdf("figures/united_prob_density.pdf", width = 13, height = 9)
plot_prob_density
dev.off()
#:------------------------------------------------------------
# Calculate AUROC with intervals
calc_auroc <- function(data, predictions, thinning = 100) {
# output file
output <- NULL
# sequence to iterate through
sequence_list <- seq(1, nrow(predictions), thinning)
for (i in 1:length(sequence_list)) {
# print the current iteration
if (i %% 1000 == 0) {
print(paste(i, "out of", length(sequence_list)))
}
interim <- roc(data,
predictions[sequence_list[i],],
levels=c(0,1), direction = "<", plot=FALSE, print.auc=FALSE)
## append new value
output <- c(output, as.numeric(interim$auc))
}
return(output)
}
###
## This section is thinned to help run times but also does not make much difference to full posterior values
###
## Type 1 UNITED
### No biomarker models
auc_T1D_no_T_united_all_genes <- calc_auroc(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_no_T_full, thinning = 10)
# quantile(auc_T1D_no_T_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.7053243 0.7924399 0.8190743
### Biomarker models
auc_T1D_with_T_united_all_genes <- calc_auroc(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_with_T_full, thinning = 10)
# quantile(auc_T1D_with_T_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.9487113 0.9768041 0.9779210
## Type 2 UNITED
auc_T2D_new_united_all_genes <- calc_auroc(dataset.UNITED_type2_all_genes$M, predictions_dataset.UNITED_type2_all_genes_new_full, thinning = 10)
# quantile(auc_T2D_new_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.8465473 0.8618926 0.8768116
#:------------------------------------------------------------
# Calculate AUROC with intervals
calc_auc_pr <- function(data, predictions, class1 = 1, thinning = 100) {
# output file
output <- NULL
# sequence to iterate through
sequence_list <- seq(1, nrow(predictions), thinning)
for (i in 1:length(sequence_list)) {
# print the current iteration
if (i %% 1000 == 0) {
print(paste(i, "out of", length(sequence_list)))
}
interim <- data.frame(
resp = data,
pred = predictions[sequence_list[i],]
)
## calculate auc pr
interim_curve <- pr.curve(
scores.class1 = interim %>% filter(resp != class1) %>% select(pred) %>% unlist(),
scores.class0 = interim %>% filter(resp == class1) %>% select(pred) %>% unlist(),
curve = FALSE)
## append new value
output <- c(output, as.numeric(interim_curve$auc.integral))
}
return(output)
}
## Type 1 UNITED
### No biomarker models
pr_auc_T1D_no_T_united_all_genes <- calc_auc_pr(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_no_T_full, thinning = 10)
# quantile(pr_auc_T1D_no_T_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.01548813 0.02644908 0.03477454
### Biomarker models
pr_auc_T1D_with_T_united_all_genes <- calc_auc_pr(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_with_T_full, thinning = 10)
# quantile(pr_auc_T1D_with_T_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.09617254 0.19216510 0.22411871
## Type 2 UNITED
pr_auc_T2D_new_united_all_genes <- calc_auc_pr(dataset.UNITED_type2_all_genes$M, predictions_dataset.UNITED_type2_all_genes_new_full, thinning = 10)
# quantile(pr_auc_T2D_new_united_all_genes, probs = c(0.025, 0.5, 0.975)) # thinning = 10
# 2.5% 50% 97.5%
# 0.5980896 0.6291710 0.6611516
# #:------------------------------------------------------------
#
# # Calculate ROC with intervals
# calc_roc <- function(data, predictions, thinning = 100) {
#
# output <- NULL
#
# # sequence to iterate through
# sequence_list <- seq(1, nrow(predictions), thinning)
#
# for (i in 1:length(sequence_list)) {
#
# # print the current iteration
# if (i %% 1000 == 0) {
# print(paste(i, "out of", length(sequence_list)))
# }
#
# ## calculate ROC
# interim <- pROC::roc(response = data, predictor = predictions[sequence_list[i],], levels = c(0,1), direction = "<") %>%
# magrittr::extract(c(2:3)) %>%
# as.data.frame() %>%
# mutate(iteration = paste0(sequence_list[i]))
#
# output <- rbind(output, interim)
#
# }
#
# return(output)
#
# }
#
#
# ## Type 1 UNITED
#
# ### No biomarker models
# roc_T1D_no_T_united_all_genes <- calc_roc(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_no_T_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_roc_T1D_no_T_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = roc_T1D_no_T_united_all_genes,
# aes(x = 1-specificities, y= sensitivities, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::roc(response = dataset.UNITED_type1_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full), levels = c(0,1), direction = "<") %>%
# magrittr::extract(c(2:3)) %>%
# as.data.frame(),
# aes(x = 1-specificities, y= sensitivities), colour = "black"
# )
#
# ### Biomarker models
# roc_T1D_with_T_united_all_genes <- calc_roc(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_with_T_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_roc_T1D_with_T_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = roc_T1D_with_T_united_all_genes,
# aes(x = 1-specificities, y= sensitivities, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::roc(response = dataset.UNITED_type1_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full)) %>%
# magrittr::extract(c(2:3)) %>%
# as.data.frame(),
# aes(x = 1-specificities, y= sensitivities), colour = "black"
# )
#
# ## Type 2 UNITED
# roc_T2D_new_united_all_genes <- calc_roc(dataset.UNITED_type2_all_genes$M, predictions_dataset.UNITED_type2_all_genes_new_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_roc_T2D_new_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = roc_T2D_new_united_all_genes,
# aes(x = 1-specificities, y= sensitivities, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::roc(response = dataset.UNITED_type2_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type2_all_genes_new_full)) %>%
# magrittr::extract(c(2:3)) %>%
# as.data.frame(),
# aes(x = 1-specificities, y= sensitivities), colour = "black"
# )
#
#
#
# #:------------------------------------------------------------
#
#
# # Calculate Precision-recall with intervals
# calc_prec_recal_curve <- function(data, predictions, thinning = 100) {
#
# output <- NULL
#
# # sequence to iterate through
# sequence_list <- seq(1, nrow(predictions), thinning)
#
# for (i in 1:length(sequence_list)) {
#
# # print the current iteration
# if (i %% 100 == 0) {
# print(paste(i, "out of", length(sequence_list)))
# }
#
# ## calculate ROC
# interim <- pROC::coords(pROC::roc(response = data, predictor = predictions[sequence_list[i],], levels = c(0,1), direction = "<"), ret = c("precision", "recall")) %>%
# as.data.frame() %>%
# mutate(iteration = paste0(sequence_list[i]))
#
# output <- rbind(output, interim)
#
# }
#
# return(output)
#
# }
#
#
# ## Type 1 UNITED
#
# ### No biomarker models
# prec_recal_T1D_no_T_united_all_genes <- calc_prec_recal_curve(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_no_T_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_prec_recal_T1D_no_T_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = prec_recal_T1D_no_T_united_all_genes,
# aes(x = recall, y= precision, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::coords(pROC::roc(response = dataset.UNITED_type1_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full)), ret = c("precision", "recall")) %>%
# as.data.frame(),
# aes(x = recall, y= precision), colour = "black"
# )
#
# ### Biomarker models
# prec_recal_T1D_with_T_united_all_genes <- calc_prec_recal_curve(dataset.UNITED_type1_all_genes$M, predictions_dataset.UNITED_type1_all_genes_with_T_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_prec_recal_T1D_with_T_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = prec_recal_T1D_with_T_united_all_genes,
# aes(x = recall, y= precision, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::coords(pROC::roc(response = dataset.UNITED_type1_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full)), ret = c("precision", "recall")) %>%
# as.data.frame(),
# aes(x = recall, y= precision), colour = "black"
# )
#
# ## Type 2 UNITED
# prec_recal_T2D_new_united_all_genes <- calc_prec_recal_curve(dataset.UNITED_type2_all_genes$M, predictions_dataset.UNITED_type2_all_genes_new_full, thinning = 100)
#
# # plot for ROC with grey being iterations, black being the ROC for average prediction
# plot_prec_recal_T2D_new_united_all_genes <- ggplot() +
# ## all iterations
# geom_path(
# data = prec_recal_T2D_new_united_all_genes,
# aes(x = recall, y= precision, group = iteration), colour = "grey"
# ) +
# ## average predictions
# geom_path(
# data = pROC::coords(pROC::roc(response = dataset.UNITED_type2_all_genes$M, predictor = colMeans(predictions_dataset.UNITED_type2_all_genes_new_full)), ret = c("precision", "recall")) %>%
# as.data.frame(),
# aes(x = recall, y= precision), colour = "black"
# )
#:------------------------------------------------------------------------------
# Boxplot and roc curves
roc_curves <- data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(2:3) %>%
as.data.frame() %>%
mutate(
auc = unname(data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(c(9)) %>%
unlist()),
auc_low = quantile(auc_T1D_with_T_united_all_genes, probs = c(0.025)),
auc_high = quantile(auc_T1D_with_T_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 1", Calculator = "Biomarkers") %>%
rbind(
data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(2:3) %>%
as.data.frame() %>%
mutate(
auc = unname(data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(c(9)) %>%
unlist()),
auc_low = quantile(auc_T1D_no_T_united_all_genes, probs = c(0.025)),
auc_high = quantile(auc_T1D_no_T_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 1", Calculator = "No Biomarkers"),
data.frame(prob = colMeans(predictions_dataset.UNITED_type2_all_genes_new_full)) %>%
cbind(Mody = dataset.UNITED_type2_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(2:3) %>%
as.data.frame() %>%
mutate(
auc = unname(data.frame(prob = colMeans(predictions_dataset.UNITED_type2_all_genes_new_full)) %>%
cbind(Mody = dataset.UNITED_type2_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
magrittr::extract(c(9)) %>%
unlist()),
auc_low = quantile(auc_T2D_new_united_all_genes, probs = c(0.025)),
auc_high = quantile(auc_T2D_new_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type2_all_genes_new_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 2", Calculator = "No Biomarkers")
)
dat_text <- roc_curves %>%
select(-sensitivities, -specificities) %>%
distinct() %>%
mutate(
auc_full = paste0("AUC: ", signif(auc, 2), " [", signif(auc_low, 2), "-", signif(auc_high, 2), "]"),
mean = paste0("Mean prob:", signif(mean, 2)*100, "%"),
Calculator = factor(Calculator, levels = c("Biomarkers", "No Biomarkers"), labels = c("Clinical features and biomarkers", "Clinical features"))
)
plot_prob_density_rocs_external_bio_only <- patchwork::wrap_plots(
# Panel A - External insulin-treated
patchwork::wrap_plots(
patchwork::wrap_plots(
# Density
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_with_T$prob) %>%
filter(M == 0) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_density(aes(x = prob), fill = "grey") +
coord_cartesian(xlim =c(0, 1)) +
scale_x_continuous(labels = scales::percent) +
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
ylab("Non-MODY (n=1164)"),
#point
dataset.UNITED_type1_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type1_all_genes_with_T$prob) %>%
filter(M == 1) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_point(aes(x = prob, y=0), position = position_jitter(height = 0.1, seed = 20)) +
coord_cartesian(xlim =c(0, 1), ylim = c(-0.15, 0.15)) +
scale_x_continuous(labels = scales::percent) +
theme_classic() +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
#axis.title.y = element_blank()
) +
ylab("MODY \n cases \n (n=10)") +
xlab("Model probabilities"),
ncol = 1, nrow = 2, heights = c(4,1)
),
#rocs
roc_curves %>%
filter(Dataset == "UNITED" & Model == "Type 1" & Calculator == "Biomarkers") %>%
mutate(
Calculator = factor(Calculator, levels = c("No Biomarkers", "Biomarkers"), labels = c("Clinical features", "Clinical features and biomarkers")),
iteration = 0
) %>%
ggplot(aes(x = 1- specificities, y = sensitivities)) +
geom_path() +
theme_bw() +
# facet_grid(~factor(Calculator, levels = c("Clinical features", "Clinical features and biomarkers"), labels = c("Early-insulin-treated: clinical features, no biomarkers", "Early-insulin-treated: clinical features, with biomarkers")), scales = "free",labeller = label_wrap_gen(width = 46, multi_line = TRUE)) +
scale_y_continuous("Sensitivity", labels = scales::percent) +
scale_x_continuous("1- Specificity", labels = scales::percent) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 1" & Calculator == "Clinical features and biomarkers"),
mapping = aes(x = 0.55, y = 0.1, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
) +
theme(
panel.spacing.x = unit(1.5, "lines")
),
ncol = 2, nrow = 1
),
# Panel B - External non-insulin-treated
patchwork::wrap_plots(
patchwork::wrap_plots(
# Density
dataset.UNITED_type2_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type2_all_genes_new$prob) %>%
filter(M == 0) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_density(aes(x = prob), fill = "grey") +
coord_cartesian(xlim =c(0, 1)) +
scale_x_continuous(labels = scales::percent) +
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
ylab("Non-MODY (n=102)"),
#point
dataset.UNITED_type2_all_genes %>%
mutate(prob = predictions_dataset.UNITED_type2_all_genes_new$prob) %>%
filter(M == 1) %>%
select(M, prob) %>%
rename("Mody" = "M") %>%
mutate(
Mody = factor(Mody, levels = c(0, 1), labels = c("Non-MODY", "MODY")),
) %>%
ggplot() +
geom_point(aes(x=prob, y=0), position = position_jitter(height = 0.1, seed = 20)) +
coord_cartesian(xlim =c(0, 1), ylim = c(-0.15, 0.15)) +
scale_x_continuous(labels = scales::percent) +
theme_classic() +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
#axis.title.y = element_blank()
) +
ylab("MODY \n cases \n (n=23)") +
xlab("Model probabilities"),
ncol = 1, nrow = 2, heights = c(4,1)
),
#rocs
roc_curves %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(Calculator = factor(Calculator, levels = "No Biomarkers", labels = "Not-early-insulin-treated")) %>%
ggplot(aes(x = 1- specificities, y = sensitivities)) +
geom_path() +
theme_bw() +
scale_y_continuous("Sensitivity", labels = scales::percent) +
scale_x_continuous("1- Specificity", labels = scales::percent) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(
Calculator = factor(Calculator, levels = c("Clinical features"), labels = c("Not-early-insulin-treated"))
),
mapping = aes(x = 0.55, y = 0.1, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
),
ncol = 2, nrow = 1
),
ncol = 1
) + patchwork::plot_annotation(tag_levels = list(c("A.1", "", "A.2", "B.1", "","B.2"))) &
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
strip.text = element_text(size = 11)
)
# Making plots
pdf("figures/united_boxplot_roc.pdf", width = 13, height = 9)
plot_prob_density_rocs_external_bio_only
dev.off()
#:-------------------------------------------------------------
plot_prob_rocs_united <- patchwork::wrap_plots(
# ROC T1D
patchwork::free(
roc_curves %>%
filter(Dataset == "UNITED" & Model == "Type 1") %>%
mutate(
Calculator = factor(Calculator, levels = c("No Biomarkers", "Biomarkers"), labels = c("Clinical features", "Clinical features and biomarkers")),
iteration = 0
) %>%
ggplot(aes(x = 1- specificities, y = sensitivities)) +
geom_path() +
theme_bw() +
facet_grid(~factor(Calculator, levels = c("Clinical features", "Clinical features and biomarkers"), labels = c("Early-insulin-treated: clinical features, no biomarkers", "Early-insulin-treated: clinical features, with biomarkers")), scales = "free",labeller = label_wrap_gen(width = 46, multi_line = TRUE)) +
scale_y_continuous("Sensitivity", labels = scales::percent) +
scale_x_continuous("1- Specificity", labels = scales::percent) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 1"),
mapping = aes(x = 0.55, y = 0.1, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
) +
theme(
panel.spacing.x = unit(1.5, "lines")
)
),
patchwork::free(
roc_curves %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(Calculator = factor(Calculator, levels = "No Biomarkers", labels = "Not-early-insulin-treated")) %>%
ggplot(aes(x = 1- specificities, y = sensitivities)) +
geom_path() +
theme_bw() +
facet_grid(~~factor(Calculator, levels = c("Not-early-insulin-treated"), labels = c("Not-early-insulin-treated: clinical features, no biomarkers")), scales = "free",labeller = label_wrap_gen(width = 46, multi_line = TRUE)) +
scale_y_continuous("Sensitivity", labels = scales::percent) +
scale_x_continuous("1- Specificity", labels = scales::percent) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(
Calculator = factor(Calculator, levels = c("Clinical features"), labels = c("Not-early-insulin-treated"))
),
mapping = aes(x = 0.55, y = 0.1, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
)
),
nrow = 2, ncol = 1
) + patchwork::plot_annotation(tag_levels = list(c("A", "B"))) +
patchwork::plot_layout(
design = "
AAAAA
#BBB#
"
) &
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
strip.text = element_text(size = 11)
)
pdf("figures/united_roc.pdf", width = 10, height = 8)
plot_prob_rocs_united
dev.off()
################
prec_recal_curves <- data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
pROC::coords(ret = c("precision", "recall")) %>%
as.data.frame() %>%
mutate(
auc = calc_auc_pr(dataset.UNITED_type1_all_genes$M, t(as.data.frame(colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full))), thinning = 1),
auc_low = quantile(pr_auc_T1D_with_T_united_all_genes, probs = c(0.025)),
auc_high = quantile(pr_auc_T1D_with_T_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type1_all_genes_with_T_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 1", Calculator = "Biomarkers") %>%
rbind(
data.frame(prob = colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full)) %>%
cbind(Mody = dataset.UNITED_type1_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
pROC::coords(ret = c("precision", "recall")) %>%
as.data.frame() %>%
mutate(
auc = calc_auc_pr(dataset.UNITED_type1_all_genes$M, t(as.data.frame(colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full))), thinning = 1),
auc_low = quantile(pr_auc_T1D_no_T_united_all_genes, probs = c(0.025)),
auc_high = quantile(pr_auc_T1D_no_T_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type1_all_genes_no_T_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 1", Calculator = "No Biomarkers"),
data.frame(prob = colMeans(predictions_dataset.UNITED_type2_all_genes_new_full)) %>%
cbind(Mody = dataset.UNITED_type2_all_genes$M) %>%
pROC::roc(response = Mody, predictor = prob) %>%
pROC::coords(ret = c("precision", "recall")) %>%
as.data.frame() %>%
mutate(
auc = calc_auc_pr(dataset.UNITED_type2_all_genes$M, t(as.data.frame(colMeans(predictions_dataset.UNITED_type2_all_genes_new_full))), thinning = 1),
auc_low = quantile(pr_auc_T2D_new_united_all_genes, probs = c(0.025)),
auc_high = quantile(pr_auc_T2D_new_united_all_genes, probs= c(0.975)),
mean = mean(colMeans(predictions_dataset.UNITED_type2_all_genes_new_full), na.rm = TRUE)
) %>%
mutate(Dataset = "UNITED", Model = "Type 2", Calculator = "No Biomarkers")
)
dat_text <- prec_recal_curves %>%
select(-precision, -recall) %>%
distinct() %>%
mutate(
auc_full = paste0("AUC: ", signif(auc, 2), " [", signif(auc_low, 2), "-", signif(auc_high, 2), "]"),
mean = paste0("Mean prob:", signif(mean, 2)*100, "%"),
Calculator = factor(Calculator, levels = c("Biomarkers", "No Biomarkers"), labels = c("Clinical features and biomarkers", "Clinical features"))
)
plot_prob_prec_recal_united <- patchwork::wrap_plots(
# ROC T1D
patchwork::free(
prec_recal_curves %>%
filter(Dataset == "UNITED" & Model == "Type 1") %>%
mutate(
Calculator = factor(Calculator, levels = c("No Biomarkers", "Biomarkers"), labels = c("Clinical features", "Clinical features and biomarkers")),
iteration = 0
) %>%
ggplot(aes(x = recall, y = precision)) +
geom_path() +
theme_bw() +
facet_grid(~factor(Calculator, levels = c("Clinical features", "Clinical features and biomarkers"), labels = c("Early-insulin-treated: clinical features, no biomarkers", "Early-insulin-treated: clinical features, with biomarkers")), scales = "free",labeller = label_wrap_gen(width = 46, multi_line = TRUE)) +
scale_y_continuous("Precision", labels = scales::percent, limits = c(0, 1)) +
scale_x_continuous("Recall", labels = scales::percent, limits = c(0, 1)) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 1"),
mapping = aes(x = 0.55, y = 0.91, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
) +
theme(
panel.spacing.x = unit(1.5, "lines")
)
),
patchwork::free(
prec_recal_curves %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(Calculator = factor(Calculator, levels = "No Biomarkers", labels = "Not-early-insulin-treated")) %>%
ggplot(aes(x = recall, y = precision)) +
geom_path() +
theme_bw() +
facet_grid(~~factor(Calculator, levels = c("Not-early-insulin-treated"), labels = c("Not-early-insulin-treated: clinical features, no biomarkers")), scales = "free",labeller = label_wrap_gen(width = 46, multi_line = TRUE)) +
scale_y_continuous("Precision", labels = scales::percent, limits = c(0, 1)) +
scale_x_continuous("Recall", labels = scales::percent, limits = c(0, 1)) +
theme_bw() +
geom_label(
data = dat_text %>%
filter(Dataset == "UNITED" & Model == "Type 2") %>%
mutate(
Calculator = factor(Calculator, levels = c("Clinical features"), labels = c("Not-early-insulin-treated"))
),
mapping = aes(x = 0.55, y = 0.91, label = auc_full, hjust = "center"),
size = 7,
label.r = unit(0, "pt"),
label.padding=unit(0.4, "lines")
)
),
nrow = 2, ncol = 1
) + patchwork::plot_annotation(tag_levels = list(c("A", "B"))) +
patchwork::plot_layout(
design = "
AAAAA
#BBB#
"
) &
theme(
axis.text = element_text(size = 14),
axis.title = element_text(size = 16),
strip.text = element_text(size = 11)
)
pdf("figures/united_prec_recal_thin.pdf", width = 10, height = 8)
plot_prob_prec_recal_united
dev.off()