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Copy path27-02-2024_cass_BHRC_PrevByW0AgeSepADHD_plot.R
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27-02-2024_cass_BHRC_PrevByW0AgeSepADHD_plot.R
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pacman::p_load(
glue, dplyr, ggplot2, remotes,
envalysis, extrafont, glue, ggthemr
)
#font_import()
loadfonts()
# check system
if (Sys.info()["sysname"] == "Linux") {
path <- "/media/santorolab/C207-3566"
font <- "Ubuntu Condensed"
} else {
path <- "D:"
font <- "Arial Narrow"
}
# Data for models ajustment and plot
pheno <-
readRDS(glue("{path}/objects_R/cass_BHRC_mod_All_Phenotypes_26-02-2024.RDS"))$dcanyhk
ages <-
readRDS(glue("{path}/objects_R/cass_BHRC_Age_Imputed_26-02-2024.RDS")) %>%
rename(age_W0 = W0, age_W1 = W1, age_W2 = W2)
prs <-
data.table::fread(glue("{path}/PRS_database/Final_Scores_PRSCS/PRSCS_ADHD_Score.profile")) %>%
select(IID, PRSCS_zscore) %>%
rename(prs = PRSCS_zscore)
state <- readRDS(glue("{path}/objects_R/cass_BHRC_STATE.RDS"))
sex <- readRDS(glue("{path}/objects_R/cass_BHRC_sex.RDS"))
pc <- readRDS(glue("{path}/objects_R/cass_BHRC_PC20.RDS"))
# Model ajustment
var_for_ajustment <- plyr::join_all(
list(prs, sex, state, pc),
by = "IID",
type = "inner"
)
new_values <- glm(prs ~ sex:PC20, data = var_for_ajustment)
new_prs <- data.frame(
IID = var_for_ajustment$IID,
prs = new_values$residuals
)
data_adhd <- plyr::join_all(
list(new_prs, pheno, ages), type = "inner", by = "IID"
)
# Prevalence calculation (10 and 5) and separate groups by age in W0
data_adhd$ADHD_Decile <- ntile(data_adhd$prs, 10)
data_adhd$ADHD_Quintile <- ntile(data_adhd$prs, 5)
data_adhd$Age_sep <- ntile(data_adhd$age_W0, 4)
#> table(round(data_adhd$age_W0))
# 6 7 8 9 10 11 12 13 14
# 2 129 218 243 262 225 175 129 64
### 1st
adhd <- filter(data_adhd, Age_sep == 1)
# Decile
pW0_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W0)))
colnames(pW0_decile) <- c("Decil", "Control0", "Case0")
pW0_decile$p0 <- pW0_decile$Case0 / (pW0_decile$Case0 + pW0_decile$Control0)
pW0_decile$Decil <- factor(pW0_decile$Decil)
pW1_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W1)))
colnames(pW1_decile) <- c("Decil", "Control1", "Case1")
pW1_decile$p1 <- pW1_decile$Case1 / (pW1_decile$Case1 + pW1_decile$Control1)
pW1_decile$Decil <- factor(pW1_decile$Decil)
pW2_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W2)))
colnames(pW2_decile) <- c("Decil", "Control2", "Case2")
pW2_decile$p2 <- pW2_decile$Case2 / (pW2_decile$Case2 + pW2_decile$Control2)
pW2_decile$Decil <- factor(pW2_decile$Decil)
for_plot1 <-
plyr::join_all(
list(pW0_decile, pW1_decile, pW2_decile),
by = "Decil",
type = "inner"
) %>%
select(Decil, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot1$wave <- gsub("^p", "W", for_plot1$wave)
# Quintile
pW0_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W0)))
colnames(pW0_quintile) <- c("Quintile", "Control0", "Case0")
pW0_quintile$p0 <- pW0_quintile$Case0 / (pW0_quintile$Case0 + pW0_quintile$Control0)
pW0_quintile$Quintile <- factor(pW0_quintile$Quintile)
pW1_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W1)))
colnames(pW1_quintile) <- c("Quintile", "Control1", "Case1")
pW1_quintile$p1 <- pW1_quintile$Case1 / (pW1_quintile$Case1 + pW1_quintile$Control1)
pW1_quintile$Quintile <- factor(pW1_quintile$Quintile)
pW2_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W2)))
colnames(pW2_quintile) <- c("Quintile", "Control2", "Case2")
pW2_quintile$p2 <- pW2_quintile$Case2 / (pW2_quintile$Case2 + pW2_quintile$Control2)
pW2_quintile$Quintile <- factor(pW2_quintile$Quintile)
for_plot2 <-
plyr::join_all(
list(pW0_quintile, pW1_quintile, pW2_quintile),
by = "Quintile",
type = "inner"
) %>%
select(Quintile, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot2$wave <- gsub("^p", "W", for_plot2$wave)
### 2nd
adhd <- filter(data_adhd, Age_sep == 2)
# Decile
pW0_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W0)))
colnames(pW0_decile) <- c("Decil", "Control0", "Case0")
pW0_decile$p0 <- pW0_decile$Case0 / (pW0_decile$Case0 + pW0_decile$Control0)
pW0_decile$Decil <- factor(pW0_decile$Decil)
pW1_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W1)))
colnames(pW1_decile) <- c("Decil", "Control1", "Case1")
pW1_decile$p1 <- pW1_decile$Case1 / (pW1_decile$Case1 + pW1_decile$Control1)
pW1_decile$Decil <- factor(pW1_decile$Decil)
pW2_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W2)))
colnames(pW2_decile) <- c("Decil", "Control2", "Case2")
pW2_decile$p2 <- pW2_decile$Case2 / (pW2_decile$Case2 + pW2_decile$Control2)
pW2_decile$Decil <- factor(pW2_decile$Decil)
for_plot3 <-
plyr::join_all(
list(pW0_decile, pW1_decile, pW2_decile),
by = "Decil",
type = "inner"
) %>%
select(Decil, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot3$wave <- gsub("^p", "W", for_plot3$wave)
# Quintile
pW0_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W0)))
colnames(pW0_quintile) <- c("Quintile", "Control0", "Case0")
pW0_quintile$p0 <- pW0_quintile$Case0 / (pW0_quintile$Case0 + pW0_quintile$Control0)
pW0_quintile$Quintile <- factor(pW0_quintile$Quintile)
pW1_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W1)))
colnames(pW1_quintile) <- c("Quintile", "Control1", "Case1")
pW1_quintile$p1 <- pW1_quintile$Case1 / (pW1_quintile$Case1 + pW1_quintile$Control1)
pW1_quintile$Quintile <- factor(pW1_quintile$Quintile)
pW2_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W2)))
colnames(pW2_quintile) <- c("Quintile", "Control2", "Case2")
pW2_quintile$p2 <- pW2_quintile$Case2 / (pW2_quintile$Case2 + pW2_quintile$Control2)
pW2_quintile$Quintile <- factor(pW2_quintile$Quintile)
for_plot4 <-
plyr::join_all(
list(pW0_quintile, pW1_quintile, pW2_quintile),
by = "Quintile",
type = "inner"
) %>%
select(Quintile, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot4$wave <- gsub("^p", "W", for_plot4$wave)
### 3rd
adhd <- filter(data_adhd, Age_sep == 3)
# Decile
pW0_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W0)))
colnames(pW0_decile) <- c("Decil", "Control0", "Case0")
pW0_decile$p0 <- pW0_decile$Case0 / (pW0_decile$Case0 + pW0_decile$Control0)
pW0_decile$Decil <- factor(pW0_decile$Decil)
pW1_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W1)))
colnames(pW1_decile) <- c("Decil", "Control1", "Case1")
pW1_decile$p1 <- pW1_decile$Case1 / (pW1_decile$Case1 + pW1_decile$Control1)
pW1_decile$Decil <- factor(pW1_decile$Decil)
pW2_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W2)))
colnames(pW2_decile) <- c("Decil", "Control2", "Case2")
pW2_decile$p2 <- pW2_decile$Case2 / (pW2_decile$Case2 + pW2_decile$Control2)
pW2_decile$Decil <- factor(pW2_decile$Decil)
for_plot5 <- plyr::join_all(
list(pW0_decile, pW1_decile, pW2_decile),
by = "Decil",
type = "inner"
) %>%
select(Decil, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot5$wave <- gsub("^p", "W", for_plot5$wave)
# Quintile
pW0_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W0)))
colnames(pW0_quintile) <- c("Quintile", "Control0", "Case0")
pW0_quintile$p0 <- pW0_quintile$Case0 / (pW0_quintile$Case0 + pW0_quintile$Control0)
pW0_quintile$Quintile <- factor(pW0_quintile$Quintile)
pW1_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W1)))
colnames(pW1_quintile) <- c("Quintile", "Control1", "Case1")
pW1_quintile$p1 <- pW1_quintile$Case1 / (pW1_quintile$Case1 + pW1_quintile$Control1)
pW1_quintile$Quintile <- factor(pW1_quintile$Quintile)
pW2_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W2)))
colnames(pW2_quintile) <- c("Quintile", "Control2", "Case2")
pW2_quintile$p2 <- pW2_quintile$Case2 / (pW2_quintile$Case2 + pW2_quintile$Control2)
pW2_quintile$Quintile <- factor(pW2_quintile$Quintile)
for_plot6 <-
plyr::join_all(
list(pW0_quintile, pW1_quintile, pW2_quintile),
by = "Quintile",
type = "inner"
) %>%
select(Quintile, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot6$wave <- gsub("^p", "W", for_plot6$wave)
# 4th: x >= 14
adhd <- filter(data_adhd, Age_sep == 4)
# Decile
pW0_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W0)))
colnames(pW0_decile) <- c("Decil", "Control0", "Case0")
pW0_decile$p0 <- pW0_decile$Case0 / (pW0_decile$Case0 + pW0_decile$Control0)
pW0_decile$Decil <- factor(pW0_decile$Decil)
pW1_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W1)))
colnames(pW1_decile) <- c("Decil", "Control1", "Case1")
pW1_decile$p1 <- pW1_decile$Case1 / (pW1_decile$Case1 + pW1_decile$Control1)
pW1_decile$Decil <- factor(pW1_decile$Decil)
pW2_decile <- as.data.frame(cbind(1:10, table(adhd$ADHD_Decile, adhd$W2)))
colnames(pW2_decile) <- c("Decil", "Control2", "Case2")
pW2_decile$p2 <- pW2_decile$Case2 / (pW2_decile$Case2 + pW2_decile$Control2)
pW2_decile$Decil <- factor(pW2_decile$Decil)
for_plot7 <-
plyr::join_all(
list(pW0_decile, pW1_decile, pW2_decile),
by = "Decil",
type = "inner"
) %>%
select(Decil, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot7$wave <- gsub("^p", "W", for_plot7$wave)
# Quintile
pW0_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W0)))
colnames(pW0_quintile) <- c("Quintile", "Control0", "Case0")
pW0_quintile$p0 <- pW0_quintile$Case0 / (pW0_quintile$Case0 + pW0_quintile$Control0)
pW0_quintile$Quintile <- factor(pW0_quintile$Quintile)
pW1_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W1)))
colnames(pW1_quintile) <- c("Quintile", "Control1", "Case1")
pW1_quintile$p1 <- pW1_quintile$Case1 / (pW1_quintile$Case1 + pW1_quintile$Control1)
pW1_quintile$Quintile <- factor(pW1_quintile$Quintile)
pW2_quintile <- as.data.frame(cbind(1:5, table(adhd$ADHD_Quintile, adhd$W2)))
colnames(pW2_quintile) <- c("Quintile", "Control2", "Case2")
pW2_quintile$p2 <- pW2_quintile$Case2 / (pW2_quintile$Case2 + pW2_quintile$Control2)
pW2_quintile$Quintile <- factor(pW2_quintile$Quintile)
for_plot8 <-
plyr::join_all(
list(pW0_quintile, pW1_quintile, pW2_quintile),
by = "Quintile",
type = "inner"
) %>%
select(Quintile, p0, p1, p2) %>%
tidyr::pivot_longer(
cols = starts_with("p"),
names_to = "wave",
values_to = "prevalence"
)
for_plot8$wave <- gsub("^p", "W", for_plot8$wave)
# for the plotting part
new_x_axis_5 <- c("1st", "2nd", "3rd", "4th", "5th")
new_x_axis_10 <- c("1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th", "9th", "10th")
ggthemr("fresh")
library(patchwork)
p1 <-
ggplot(for_plot1, aes(x = Decil)) +
geom_line(
aes(
x = Decil,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Decil, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_10) +
scale_y_continuous(n.breaks = 10, limits = c(1, 50)) +
theme_publish() +
labs(
title = glue("BHRC ADHD prevalence data\nN = {nrow(data_adhd)}"),
subtitle = "min: 5.8 yr\nmax: 8.57 yr",
y = "Prevalence %",
x = "PRS Decile") +
theme(
legend.position = "top",
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
p2 <-
ggplot(for_plot3, aes(x = Decil)) +
geom_line(
aes(
x = Decil,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Decil, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_10) +
scale_y_continuous(n.breaks = 10, limits = c(1, 50)) +
theme_publish() +
labs(
subtitle = "min: 8.57 yr\nmax: 10.08 yr",
y = "Prevalence %",
x = "PRS Decile") +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none")
p3 <-
ggplot(for_plot5, aes(x = Decil)) +
geom_line(
aes(
x = Decil,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Decil, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_10) +
scale_y_continuous(n.breaks = 10, limits = c(1, 50)) +
theme_publish() +
labs(
subtitle = "min: 10.09 yr\nmax: 11.53 yr",
y = "Prevalence %",
x = "PRS Decile") +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none")
p4 <-
ggplot(for_plot7, aes(x = Decil)) +
geom_line(
aes(
x = Decil,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Decil, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_10) +
scale_y_continuous(n.breaks = 10, limits = c(1, 50)) +
theme_publish() +
labs(
subtitle = "min: 11.54 yr\nmax: 14.33 yr",
y = "Prevalence %",
x = "PRS Decile",
caption = "27-02-2024_cass_BHRC)PrevByW0AgeSepADHD_plot.R") +
theme(
axis.title.y = element_blank(),
legend.position = "none")
p5 <-
ggplot(for_plot2, aes(x = Quintile)) +
geom_line(
aes(
x = Quintile,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Quintile, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_5) +
scale_y_continuous(n.breaks = 15, limits = c(1, 30)) +
theme_publish() +
labs(
title = glue("BHRC ADHD prevalence data\nN = {nrow(data_adhd)}"),
subtitle = "min: 5.83 yr\nmax: 8.57 yr",
y = "Prevalence %",
x = "PRS Quintile") +
theme(
legend.position = "top",
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
p6 <-
ggplot(for_plot4, aes(x = Quintile)) +
geom_line(
aes(
x = Quintile,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Quintile, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_5) +
scale_y_continuous(n.breaks = 15, limits = c(1, 30)) +
theme_publish() +
labs(
subtitle = "min: 8.57 yr\nmax: 10.08 yr",
y = "Prevalence %",
x = "PRS Quintile") +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
p7 <-
ggplot(for_plot6, aes(x = Quintile)) +
geom_line(
aes(
x = Quintile,
y = prevalence * 100,
color = wave,
group = wave),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Quintile, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_5) +
scale_y_continuous(n.breaks = 15, limits = c(1, 30)) +
theme_publish() +
labs(
subtitle = "min: 10.09 yr\nmax: 11.53 yr",
y = "Prevalence %",
x = "PRS Quintile") +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none")
p8 <-
ggplot(for_plot8, aes(x = Quintile)) +
geom_line(
aes(
x = Quintile,
y = prevalence * 100,
color = wave,
group = wave
),
linewidth = 1,
linetype = "dashed") +
geom_point(aes(x = Quintile, y = prevalence * 100, color = wave), size = 5) +
scale_x_discrete(labels = new_x_axis_5) +
scale_y_continuous(n.breaks = 15, limits = c(1, 30)) +
theme_publish() +
labs(
subtitle = "min: 11.54 yr\nmax: 14.33 yr",
y = "Prevalence %",
x = "PRS Quintile",
caption = "27-02-2024_cass_BHRC)PrevByW0AgeSepADHD_plot.R"
) +
theme(
axis.title.y = element_blank(),
legend.position = "none")
p1 / p2 / p3 / p4
p5 / p6 / p7 / p8