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07c_group_ind_pred_plots.R
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07c_group_ind_pred_plots.R
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library(dplyr)
library(tidyr)
library(data.table)
library(readr)
library(stringr)
library(ggplot2)
library(scales)
library(cowplot)
library(grid)
library(gridExtra)
# Phenotypes
pheno <- c("Height","Platelet","MCV","MCH","BMI","RBC","Monocyte",
"Lymphocyte","WBC","Eosinophil", "LDL", "Weight", "Triglycerides", "Cystatin_C",
"Body_Fat_Perc")
# Read in group level and individual PGS files for covariates
group_pgs_df <- read_tsv("data/pgs_pred/group_pgs_df.tsv") %>%
filter(threshold == 1)
ind_pgs_df <- read_tsv("data/pgs_pred/ind_pgs_df.tsv") %>%
select(-(16:34))
# Upper limit for the GWAS set
pc_dist_gwas <- read_tsv("data/pca/pc_dist_best_gwas_std.tsv")
upper <- unname(quantile(pc_dist_gwas$pc_dist, 0.975))
# Get positions of the knots by density
temp <- ind_pgs_df %>% filter(phenotype == "Height" & pc_dist > upper)
temp <- temp %>% arrange(pc_dist)
knots <- c(upper,
temp$pc_dist[round(nrow(temp) / 9)],
temp$pc_dist[round(nrow(temp) / 9 * 2)],
temp$pc_dist[round(nrow(temp) / 9 * 3)],
temp$pc_dist[round(nrow(temp) / 9 * 4)],
temp$pc_dist[round(nrow(temp) / 9 * 5)],
temp$pc_dist[round(nrow(temp) / 9 * 6)],
temp$pc_dist[round(nrow(temp) / 9 * 7)],
temp$pc_dist[round(nrow(temp) / 9 * 8)])
# Group level plot
plot_group_level <- function(pgs_df, trait, upper){
plot_df <- pgs_df %>% filter(phenotype == trait)
# Plot prediction accuracy relative to the 50 bins with a genetic distance most similar
# to the GWAS set
denominator <- plot_df %>%
group_by(phenotype) %>%
filter(group_close_to_gwas <= 50) %>%
dplyr::summarise(ref_partial_r2 = mean(partial))
plot_df <- plot_df %>%
left_join(denominator, by = c("phenotype")) %>%
mutate(relative_performance = partial / ref_partial_r2) %>%
select(-ref_partial_r2) %>%
ungroup()
# Only plot the data points > upper
plot_df <- plot_df %>% filter(median_pc > upper)
# Fit a spline
lm <- lm(relative_performance ~
splines::bs(median_pc, knots = knots), data = plot_df)
plot_df_2 <- cbind.data.frame(median_pc = seq(1.908283, 197.5882, by = 0.4891998))
plot_df_2$fitted_val = unname(predict(lm, newdata = plot_df_2))
plot <- ggplot(plot_df, aes(x = median_pc, y = relative_performance)) +
geom_hline(yintercept = 1, size = 0.5) +
geom_point(size = 5,alpha = 0.4, fill = "#ff8934", color = "#f8766d", shape = 23)+
scale_x_continuous(breaks=c(0, 20, 40, 60, 80, 100, 120, 140, 160, 180), expand = c(0, 0)) +
geom_line(data = plot_df_2,
aes(x = median_pc, y = fitted_val), size = 2.5, color = "#e66100")
# Set the upper limit of the y-axis
if(trait == "Height"){
ymax <- 1.8
} else if(trait %in% c("Body_Fat_Perc", "Weight")){
ymax <- 4.2
} else if(trait == "BMI"){
ymax <- 3.8
} else if(trait %in% c("Eosinophil", "LDL")){
ymax <- 3.4
} else if(trait %in% c("MCH", "MCV")){
ymax <- 1.8
} else if(trait == "Platelet"){
ymax <- 2.2
} else if(trait %in% c("Triglycerides", "Monocyte", "Lymphocyte", "WBC", "Cystatin_C")){
ymax <- 2.8
} else{
ymax <- 2.2
}
plot <- plot +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.title = element_text(size=24, family = "Helvetica"),
axis.title.x = element_blank(),
axis.text = element_text(size=20, family = "Helvetica")) +
ylab(bquote("Prediction accuracy (partial"~R^2*")")) +
scale_y_continuous(expand = c(0, 0),
labels = scales::number_format(accuracy = 0.1)) +
coord_cartesian(xlim = c(upper, 200), ylim = c(0.0, ymax))
# Add some labels for height
if(trait == "Height"){
plot <- plot +
annotate("text", label = "↑", x = 150, y = 1.694, size = 15, hjust = 1,
family = "Helvetica") +
annotate("text", label = "Better prediction", x = 192, y = 1.694, size = 8, hjust = 1,
vjust = 0.8, family = "Helvetica") +
annotate(geom = "segment", x = 108, y = 0.7, xend = 115, yend = 0.7,
color = "black", size = 1) +
annotate("text", label = "A bin of ~260\nindividuals", x = 135, y = 0.7, size = 8,
family = "Helvetica")
}
return(plot)
}
# Individual level plot
reverselog_trans <- function(base = exp(1)) {
trans <- function(x) -log(x, base)
inv <- function(x) base^(-x)
trans_new(paste0("reverselog-", format(base)), trans, inv,
log_breaks(base = base),
domain = c(1e-100, Inf))
}
plot_ind_level <- function(pgs_df, trait, upper, full_range = F){
plot_df <- pgs_df %>% filter(phenotype == trait) %>%
drop_na(pc_dist, pred_error)
# Plot prediction error relative to the 50 bins with a genetic distance most similar
# to the GWAS set
denominator <- plot_df %>%
group_by(phenotype) %>%
filter(group_close_to_gwas <= 50) %>%
dplyr::summarise(ref_pred_error = mean(pred_error))
plot_df <- plot_df %>%
left_join(denominator, by = c("phenotype")) %>%
mutate(relative_performance = pred_error / ref_pred_error) %>%
select(-ref_pred_error) %>%
ungroup()
# Only plot the data points > upper
plot_df <- plot_df %>% filter(pc_dist > upper)
# Fit a spline
lm <- lm(relative_performance ~
splines::bs(pc_dist, knots = knots), data = plot_df)
plot_df_2 <- cbind.data.frame(pc_dist = seq(1.908283, 197.5882, by = 0.4891998))
plot_df_2$fitted_val = unname(predict(lm, newdata = plot_df_2))
plot <- ggplot(plot_df, aes(x = pc_dist, y = relative_performance)) +
geom_hline(yintercept = 1, size = 0.5) +
geom_point(size=2.5, alpha=0.1, shape = 16, color = "#beb0d7")+
scale_x_continuous(breaks=c(0, 20, 40, 60, 80, 100, 120, 140, 160, 180), expand = c(0, 0)) +
geom_line(data = plot_df_2,
aes(x = pc_dist, y = fitted_val), size=2.5, color = "#5D3A9B")
plot <- plot +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.title = element_text(size=24, family = "Helvetica"),
axis.title.x = element_blank(),
axis.text = element_text(size=20, family = "Helvetica")) +
ylab("Squared prediction error")
# Cropped range
if(full_range == F){
# Set the lower and upper limits of the y-axis
if(trait %in% c("MCV", "MCH", "Eosinophil", "Cystatin_C")){
ymin <- 0.4
ymax <- 4.2
} else if(trait %in% c("Lymphocyte", "WBC")){
ymin <- 0.18
ymax <- 2.2
} else{
ymin <- 0.4
ymax <- 2.2
}
plot <- plot +
scale_y_continuous(expand = c(0, 0),
breaks = c(0.25, 0.5, 1, 2, 4),
labels = c("0.25", "0.50", "1.00", "2.00", "4.00"),
trans = reverselog_trans(2)) +
coord_cartesian(xlim = c(upper, 200), ylim = c(ymax, ymin))
# Add some labels for height
if(trait == "Height"){
plot <- plot +
annotate(geom = "segment", x = 108, y = 0.75, xend = 115, yend = 0.75,
color = "black", size = 1) +
annotate("text", label = "An individual", x = 135, y = 0.75, size = 8,
family = "Helvetica") +
annotate("text", label = "↑", x = 150, y = 0.45, size = 15, hjust = 1,
family = "Helvetica") +
annotate("text", label = "Better prediction", x = 192, y = 0.45, size = 8, hjust = 1, vjust = 0.8,
family = "Helvetica")
}
# Full range
} else{
plot <- plot +
scale_y_continuous(expand = c(0, 0),
breaks=c(0.0625, 0.25, 1, 4, 16, 64, 256, 1024),
labels = c("0.0625", "0.25", "1", "4", "16", "64", "256", "1024"),
trans = reverselog_trans(2)) +
coord_cartesian(xlim = c(upper, 200),
ylim = c(max(plot_df$relative_performance, na.rm = T), 0.05))
}
return(plot)
}
# Make the plots
plot_group_height <- plot_group_level(group_pgs_df, "Height", upper)
plot_ind_height <- plot_ind_level(ind_pgs_df, "Height", upper, full_range = F)
plot_ind_full_height <- plot_ind_level(ind_pgs_df, "Height", upper, full_range = T)
plot_group_cystatin_c <- plot_group_level(group_pgs_df, "Cystatin_C", upper)
plot_ind_cystatin_c <- plot_ind_level(ind_pgs_df, "Cystatin_C", upper, full_range = F)
plot_ind_full_cystatin_c <- plot_ind_level(ind_pgs_df, "Cystatin_C", upper, full_range = T)
plot_group_platelet <- plot_group_level(group_pgs_df, "Platelet", upper)
plot_ind_platelet <- plot_ind_level(ind_pgs_df, "Platelet", upper, full_range = F)
plot_ind_full_platelet <- plot_ind_level(ind_pgs_df, "Platelet", upper, full_range = T)
plot_group_mcv <- plot_group_level(group_pgs_df, "MCV", upper)
plot_ind_mcv <- plot_ind_level(ind_pgs_df, "MCV", upper, full_range = F)
plot_ind_full_mcv <- plot_ind_level(ind_pgs_df, "MCV", upper, full_range = T)
plot_group_weight <- plot_group_level(group_pgs_df, "Weight", upper)
plot_ind_weight <- plot_ind_level(ind_pgs_df, "Weight", upper, full_range = F)
plot_ind_full_weight <- plot_ind_level(ind_pgs_df, "Weight", upper, full_range = T)
plot_group_mch <- plot_group_level(group_pgs_df, "MCH", upper)
plot_ind_mch <- plot_ind_level(ind_pgs_df, "MCH", upper, full_range = F)
plot_ind_full_mch <- plot_ind_level(ind_pgs_df, "MCH", upper, full_range = T)
plot_group_bmi <- plot_group_level(group_pgs_df, "BMI", upper)
plot_ind_bmi <- plot_ind_level(ind_pgs_df, "BMI", upper, full_range = F)
plot_ind_full_bmi <- plot_ind_level(ind_pgs_df, "BMI", upper, full_range = T)
plot_group_rbc <- plot_group_level(group_pgs_df, "RBC", upper)
plot_ind_rbc <- plot_ind_level(ind_pgs_df, "RBC", upper, full_range = F)
plot_ind_full_rbc <- plot_ind_level(ind_pgs_df, "RBC", upper, full_range = T)
plot_group_body_fat_perc <- plot_group_level(group_pgs_df, "Body_Fat_Perc", upper)
plot_ind_body_fat_perc <- plot_ind_level(ind_pgs_df, "Body_Fat_Perc", upper, full_range = F)
plot_ind_full_body_fat_perc <- plot_ind_level(ind_pgs_df, "Body_Fat_Perc", upper, full_range = T)
plot_group_monocyte <- plot_group_level(group_pgs_df, "Monocyte", upper)
plot_ind_monocyte <- plot_ind_level(ind_pgs_df, "Monocyte", upper, full_range = F)
plot_ind_full_monocyte <- plot_ind_level(ind_pgs_df, "Monocyte", upper, full_range = T)
plot_group_triglycerides <- plot_group_level(group_pgs_df, "Triglycerides", upper)
plot_ind_triglycerides <- plot_ind_level(ind_pgs_df, "Triglycerides", upper, full_range = F)
plot_ind_full_triglycerides <- plot_ind_level(ind_pgs_df, "Triglycerides", upper, full_range = T)
plot_group_lymphocyte <- plot_group_level(group_pgs_df, "Lymphocyte", upper)
plot_ind_lymphocyte <- plot_ind_level(ind_pgs_df, "Lymphocyte", upper, full_range = F)
plot_ind_full_lymphocyte <- plot_ind_level(ind_pgs_df, "Lymphocyte", upper, full_range = T)
plot_group_wbc <- plot_group_level(group_pgs_df, "WBC", upper)
plot_ind_wbc <- plot_ind_level(ind_pgs_df, "WBC", upper, full_range = F)
plot_ind_full_wbc <- plot_ind_level(ind_pgs_df, "WBC", upper, full_range = T)
plot_group_eosinophil <- plot_group_level(group_pgs_df, "Eosinophil", upper)
plot_ind_eosinophil <- plot_ind_level(ind_pgs_df, "Eosinophil", upper, full_range = F)
plot_ind_full_eosinophil <- plot_ind_level(ind_pgs_df, "Eosinophil", upper, full_range = T)
plot_group_ldl <- plot_group_level(group_pgs_df, "LDL", upper)
plot_ind_ldl <- plot_ind_level(ind_pgs_df, "LDL", upper, full_range = F)
plot_ind_full_ldl <- plot_ind_level(ind_pgs_df, "LDL", upper, full_range = T)
# Combine the panels
# Main fig
fig_2 <- plot_grid(NULL, NULL, plot_group_height, plot_ind_height,
NULL, NULL, plot_group_weight, plot_ind_weight,
NULL, NULL, plot_group_wbc, plot_ind_wbc,
labels = c('A. Height (group level)',
'B. Height (individual level)',
'',
'',
'C. Weight (group level)',
'D. Weight (individual level)',
'',
'',
'E. White blood cell count (group level)',
'F. White blood cell count (individual level)',
'',
''), ncol = 2, nrow = 6,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
grDevices::cairo_pdf("img/fig_2_group_ind_pred.pdf", width = 24, height = 18)
grid.arrange(arrangeGrob(fig_2,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
# Supp fig for group and individual pred
fig_s2 <- plot_grid(NULL, NULL, plot_group_bmi, plot_ind_bmi,
NULL, NULL, plot_group_body_fat_perc, plot_ind_body_fat_perc,
labels = c('A. BMI (group level)',
'B. BMI (individual level)',
'',
'',
'C. Body fat percentage (group level)',
'D. Body fat percentage (individual level)',
'',
''), ncol = 2, nrow = 4,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s3 <- plot_grid(NULL, NULL, plot_group_monocyte, plot_ind_monocyte,
NULL, NULL, plot_group_lymphocyte, plot_ind_lymphocyte,
NULL, NULL, plot_group_eosinophil, plot_ind_eosinophil,
labels = c('A. Monocyte count (group level)',
'B. Monocyte count (individual level)',
'',
'',
'C. Lymphocyte count (group level)',
'D. Lymphocyte count (individual level)',
'',
'',
'E. Eosinophil count (group level)',
'F. Eosinophil count (individual level)',
'',
''), ncol = 2, nrow = 6,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s4 <- plot_grid(NULL, NULL, plot_group_mcv, plot_ind_mcv,
NULL, NULL, plot_group_mch, plot_ind_mch,
NULL, NULL, plot_group_rbc, plot_ind_rbc,
labels = c('A. Mean corpuscular volume (group level)',
'B. Mean corpuscular volume (individual level)',
'',
'',
'C. Mean corpuscular hemoglobin (group level)',
'D. Mean corpuscular hemoglobin (individual level)',
'',
'',
'E. Red blood cell count (group level)',
'F. Red blood cell count (individual level)',
'',
''), ncol = 2, nrow = 6,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s5 <- plot_grid(NULL, NULL, plot_group_cystatin_c, plot_ind_cystatin_c,
NULL, NULL, plot_group_platelet, plot_ind_platelet,
NULL, NULL, plot_group_triglycerides, plot_ind_triglycerides,
NULL, NULL, plot_group_ldl, plot_ind_ldl,
labels = c('A. Cystatic C level (group level)',
'B. Cystatic C level (individual level)',
'',
'',
'C. Platelet count (group level)',
'D. Platelet count (individual level)',
'',
'',
'E. Triglyceride level (group level)',
'F. Triglyceride level (individual level)',
'',
'',
'G. LDL cholesterol level (group level)',
'H. LDL cholesterol level (individual level)',
'',
''), ncol = 2, nrow = 8,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
grDevices::cairo_pdf("img/fig_s2_group_ind_pred_physical.pdf", width = 24, height = 12, onefile = T)
grid.arrange(arrangeGrob(fig_s1,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s3_group_ind_pred_wbc.pdf", width = 24, height = 18, onefile = T)
grid.arrange(arrangeGrob(fig_s2,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s4_group_ind_pred_rbc.pdf", width = 24, height = 18, onefile = T)
grid.arrange(arrangeGrob(fig_s3,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s5_group_ind_pred_other.pdf", width = 24, height = 24, onefile = T)
grid.arrange(arrangeGrob(fig_s4,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
# Supp fig for full-ranged individual pred
fig_s6_full <- plot_grid(NULL, NULL, plot_ind_full_height, plot_ind_full_weight,
NULL, NULL, plot_ind_full_bmi, plot_ind_full_body_fat_perc,
labels = c('A. Height',
'B. Weight',
'',
'',
'C. BMI',
'D. Body fat percentage',
'',
''), ncol = 2, nrow = 4,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s7_full <- plot_grid(NULL, NULL, plot_ind_full_monocyte, plot_ind_full_lymphocyte,
NULL, NULL, plot_ind_full_wbc, plot_ind_full_eosinophil,
labels = c('A. Monocyte count',
'B. Lymphocyte count',
'',
'',
'C. White blood cell count',
'D. Eosinophil count',
'',
''), ncol = 2, nrow = 4,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s8_full <- plot_grid(NULL, NULL, plot_ind_full_mcv, plot_ind_full_mch,
NULL, NULL, plot_ind_full_rbc, NULL,
labels = c('A. Mean corpuscular volume',
'B. Mean corpuscular hemoglobin',
'',
'',
'C. Red blood cell count',
'',
'',
''), ncol = 2, nrow = 4,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s9_full <- plot_grid(NULL, NULL, plot_ind_full_cystatin_c, plot_ind_full_platelet,
NULL, NULL, plot_ind_full_triglycerides, plot_ind_full_ldl,
labels = c('A. Cystatin C level',
'B. Platelet count',
'',
'',
'C. Triglyceride level',
'D. LDL cholesterol level',
'',
''), ncol = 2, nrow = 4,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
grDevices::cairo_pdf("img/fig_s6_ind_full_pred_physical.pdf", width = 24, height = 12, onefile = T)
grid.arrange(arrangeGrob(fig_s5_full,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s7_ind_full_pred_wbc.pdf", width = 24, height = 12, onefile = T)
grid.arrange(arrangeGrob(fig_s6_full,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s8_ind_full_pred_rbc.pdf", width = 24, height = 12, onefile = T)
grid.arrange(arrangeGrob(fig_s7_full,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s9_ind_full_pred_other.pdf", width = 24, height = 12, onefile = T)
grid.arrange(arrangeGrob(fig_s8_full,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
# Variance explained at group and individual levels
var_explained_group_ind <- function(group_df, ind_df, trait, upper){
group_df <- group_df %>% filter(phenotype == trait)
group_df$var_explained <- group_df$partial
group_df$Level <- "Group"
ind_df <- ind_df %>% filter(phenotype == trait) %>%
group_by(weighted_pc_groups) %>%
summarize(median_pc = median(pc_dist, na.rm = T),
mse = mean(pred_error, na.rm = T),
var_z = var(Z, na.rm = T),
var_explained = 1 - mse / var_z)
ind_df$Level <- "Individual"
plot_df <- group_df %>% select(weighted_pc_groups, median_pc, var_explained, Level)
plot_df <- plot_df %>% rbind.data.frame(ind_df[, c(1:2, 5:6)])
group_ref <- mean(plot_df$var_explained[plot_df$Level == "Group" & plot_df$weighted_pc_groups <= 50], na.rm = T)
ind_ref <- mean(plot_df$var_explained[plot_df$Level == "Individual" & plot_df$weighted_pc_groups <= 50], na.rm = T)
plot <- plot_df %>% ggplot(aes(x = median_pc, y = var_explained, color = Level, fill = Level, shape = Level)) +
geom_hline(yintercept = group_ref, color = "#ff8934", size = 0.5) +
geom_hline(yintercept = ind_ref, color = "#5D3A9B", size = 0.5) +
geom_point(size = 5, alpha = 0.4) +
scale_x_continuous(breaks = c(0, 40, 80, 120, 160),
expand = c(0, 0)) +
scale_color_manual(values = c("#f8766d", "#5D3A9B")) +
scale_fill_manual(values = c("#ff8934", "#beb0d7")) +
scale_shape_manual(values = c(23, 21)) +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.title=element_text(size=24, family = "Helvetica"),
axis.title.x = element_blank(),
axis.text = element_text(size=20, family = "Helvetica"),
legend.title = element_text(size = 18),
legend.text = element_text(size = 16)) +
ylab("Trait variance explained in bin\nby PGS") +
coord_cartesian(xlim = c(upper, 200)) +
theme(legend.position = c(0.9, 0.9))
return(plot)
}
plot_var_explained_height <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Height", upper)
plot_var_explained_weight <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Weight", upper)
plot_var_explained_bmi <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "BMI", upper)
plot_var_explained_body_fat_perc <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Body_Fat_Perc", upper)
plot_var_explained_monocyte <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Monocyte", upper)
plot_var_explained_lymphocyte <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Lymphocyte", upper)
plot_var_explained_wbc <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "WBC", upper)
plot_var_explained_eosinophil <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Eosinophil", upper)
plot_var_explained_mcv <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "MCV", upper)
plot_var_explained_mch <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "MCH", upper)
plot_var_explained_rbc <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "RBC", upper)
plot_var_explained_cystatin_c <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Cystatin_C", upper)
plot_var_explained_platelet <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Platelet", upper)
plot_var_explained_triglycerides <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "Triglycerides", upper)
plot_var_explained_ldl <- var_explained_group_ind(group_pgs_df, ind_pgs_df, "LDL", upper)
plot_mse <- function(ind_df, trait, upper){
ind_df <- ind_df %>% filter(phenotype == trait) %>%
group_by(weighted_pc_groups) %>%
summarize(median_pc = median(pc_dist, na.rm = T),
mse = mean(pred_error, na.rm = T),
var_z = var(Z, na.rm = T),
var_explained = 1 - mse / var_z)
plot_df <- ind_df
ref <- mean(plot_df$mse[plot_df$weighted_pc_groups <= 50], na.rm = T)
# Remove the outlier(s) for plotting
if(trait %in% c("Monocyte", "Lymphocyte", "WBC", "Eosinophil")){
if(trait == "Monocyte"){
plot_df <- plot_df %>% filter(mse < 0.35)
} else if(trait == "Lymphocyte"){
plot_df <- plot_df %>% filter(mse < 5)
} else if(trait == "WBC"){
plot_df <- plot_df %>% filter(mse < 15)
} else{
plot_df <- plot_df %>% filter(mse < 0.1)
}
}
plot <- plot_df %>% ggplot(aes(x = median_pc, y = mse)) +
geom_hline(yintercept = ref, color = "#5D3A9B", size = 0.5) +
geom_point(size = 5, alpha = 0.4, shape = 21, color = "#5D3A9B", fill = "#beb0d7") +
scale_x_continuous(breaks = c(0, 40, 80, 120, 160),
expand = c(0, 0)) +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.title=element_text(size=24, family = "Helvetica"),
axis.title.x = element_blank(),
axis.text = element_text(size=20, family = "Helvetica"),
legend.title = element_text(size = 18),
legend.text = element_text(size = 16)) +
ylab("Mean squared prediction error (MSE)") +
ylim(min(c(plot_df$mse, plot_df$var_z)), max(c(plot_df$mse, plot_df$var_z))) +
coord_cartesian(xlim = c(upper, 200))
return(plot)
}
plot_mse_height <- plot_mse(ind_pgs_df, "Height", upper)
plot_mse_weight <- plot_mse(ind_pgs_df, "Weight", upper)
plot_mse_bmi <- plot_mse(ind_pgs_df, "BMI", upper)
plot_mse_body_fat_perc <- plot_mse(ind_pgs_df, "Body_Fat_Perc", upper)
plot_mse_monocyte <- plot_mse(ind_pgs_df, "Monocyte", upper)
plot_mse_lymphocyte <- plot_mse(ind_pgs_df, "Lymphocyte", upper)
plot_mse_wbc <- plot_mse(ind_pgs_df, "WBC", upper)
plot_mse_eosinophil <- plot_mse(ind_pgs_df, "Eosinophil", upper)
plot_mse_mcv <- plot_mse(ind_pgs_df, "MCV", upper)
plot_mse_mch <- plot_mse(ind_pgs_df, "MCH", upper)
plot_mse_rbc <- plot_mse(ind_pgs_df, "RBC", upper)
plot_mse_cystatin_c <- plot_mse(ind_pgs_df, "Cystatin_C", upper)
plot_mse_platelet <- plot_mse(ind_pgs_df, "Platelet", upper)
plot_mse_triglycerides <- plot_mse(ind_pgs_df, "Triglycerides", upper)
plot_mse_ldl <- plot_mse(ind_pgs_df, "LDL", upper)
plot_var_z <- function(ind_df, trait, upper){
ind_df <- ind_df %>% filter(phenotype == trait) %>%
group_by(weighted_pc_groups) %>%
summarize(median_pc = median(pc_dist, na.rm = T),
mse = mean(pred_error, na.rm = T),
var_z = var(Z, na.rm = T),
var_explained = 1 - mse / var_z)
plot_df <- ind_df
ref <- mean(plot_df$var_z[plot_df$weighted_pc_groups <= 50], na.rm = T)
# Remove the outlier(s) for plotting
if(trait %in% c("Monocyte", "Lymphocyte", "WBC", "Eosinophil")){
if(trait == "Monocyte"){
plot_df <- plot_df %>% filter(var_z < 0.35)
} else if(trait == "Lymphocyte"){
plot_df <- plot_df %>% filter(var_z < 5)
} else if(trait == "WBC"){
plot_df <- plot_df %>% filter(var_z < 15)
} else{
plot_df <- plot_df %>% filter(var_z < 0.1)
}
}
plot <- plot_df %>% ggplot(aes(x = median_pc, y = var_z)) +
geom_hline(yintercept = ref, color = "#5D3A9B", size = 0.5) +
geom_point(size = 5, alpha = 0.4, shape = 21, color = "#5D3A9B", fill = "#beb0d7") +
scale_x_continuous(breaks = c(0, 40, 80, 120, 160),
expand = c(0, 0)) +
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.title=element_text(size=24, family = "Helvetica"),
axis.title.x = element_blank(),
axis.text = element_text(size=20, family = "Helvetica"),
legend.title = element_text(size = 18),
legend.text = element_text(size = 16)) +
ylab("Variance of residualized\nphenotype (Var[Z])") +
ylim(min(c(plot_df$mse, plot_df$var_z)), max(c(plot_df$mse, plot_df$var_z))) +
coord_cartesian(xlim = c(upper, 200))
return(plot)
}
plot_var_z_height <- plot_var_z(ind_pgs_df, "Height", upper)
plot_var_z_weight <- plot_var_z(ind_pgs_df, "Weight", upper)
plot_var_z_bmi <- plot_var_z(ind_pgs_df, "BMI", upper)
plot_var_z_body_fat_perc <- plot_var_z(ind_pgs_df, "Body_Fat_Perc", upper)
plot_var_z_monocyte <- plot_var_z(ind_pgs_df, "Monocyte", upper)
plot_var_z_lymphocyte <- plot_var_z(ind_pgs_df, "Lymphocyte", upper)
plot_var_z_wbc <- plot_var_z(ind_pgs_df, "WBC", upper)
plot_var_z_eosinophil <- plot_var_z(ind_pgs_df, "Eosinophil", upper)
plot_var_z_mcv <- plot_var_z(ind_pgs_df, "MCV", upper)
plot_var_z_mch <- plot_var_z(ind_pgs_df, "MCH", upper)
plot_var_z_rbc <- plot_var_z(ind_pgs_df, "RBC", upper)
plot_var_z_cystatin_c <- plot_var_z(ind_pgs_df, "Cystatin_C", upper)
plot_var_z_platelet <- plot_var_z(ind_pgs_df, "Platelet", upper)
plot_var_z_triglycerides <- plot_var_z(ind_pgs_df, "Triglycerides", upper)
plot_var_z_ldl <- plot_var_z(ind_pgs_df, "LDL", upper)
# Combine the panels
fig_s10 <- plot_grid(NULL, NULL, NULL, plot_var_explained_height, plot_mse_height, plot_var_z_height,
NULL, NULL, NULL, plot_var_explained_weight, plot_mse_weight, plot_var_z_weight,
NULL, NULL, NULL, plot_var_explained_bmi, plot_mse_bmi, plot_var_z_bmi,
NULL, NULL, NULL, plot_var_explained_body_fat_perc, plot_mse_body_fat_perc, plot_var_z_body_fat_perc,
labels = c('A. Height',
'',
'',
'',
'',
'',
'B. Weight',
'',
'',
'',
'',
'',
'C. BMI',
'',
'',
'',
'',
'',
'D. Body fat percentage',
'',
'',
'',
'',
''), ncol = 3, nrow = 8,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s11 <- plot_grid(NULL, NULL, NULL, plot_var_explained_monocyte, plot_mse_monocyte, plot_var_z_monocyte,
NULL, NULL, NULL, plot_var_explained_lymphocyte, plot_mse_lymphocyte, plot_var_z_lymphocyte,
NULL, NULL, NULL, plot_var_explained_wbc, plot_mse_wbc, plot_var_z_wbc,
NULL, NULL, NULL, plot_var_explained_eosinophil, plot_mse_eosinophil, plot_var_z_eosinophil,
labels = c('A. Monocyte count',
'',
'',
'',
'',
'',
'B. Lymphocyte count',
'',
'',
'',
'',
'',
'C. White blood cell count',
'',
'',
'',
'',
'',
'D. Eosinophil count',
'',
'',
'',
'',
''), ncol = 3, nrow = 8,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s12 <- plot_grid(NULL, NULL, NULL, plot_var_explained_mcv, plot_mse_mcv, plot_var_z_mcv,
NULL, NULL, NULL, plot_var_explained_mch, plot_mse_mch, plot_var_z_mch,
NULL, NULL, NULL, plot_var_explained_rbc, plot_mse_rbc, plot_var_z_rbc,
labels = c('A. Mean corpuscular volume',
'',
'',
'',
'',
'',
'B. Mean corpuscular hemoglobin',
'',
'',
'',
'',
'',
'C. Red blood cell count',
'',
'',
'',
'',
'',
''), ncol = 3, nrow = 6,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
fig_s13 <- plot_grid(NULL, NULL, NULL, plot_var_explained_cystatin_c, plot_mse_cystatin_c, plot_var_z_cystatin_c,
NULL, NULL, NULL, plot_var_explained_platelet, plot_mse_platelet, plot_var_z_platelet,
NULL, NULL, NULL, plot_var_explained_triglycerides, plot_mse_triglycerides, plot_var_z_triglycerides,
NULL, NULL, NULL, plot_var_explained_ldl, plot_mse_ldl, plot_var_z_ldl,
labels = c('A. Cystatin C level',
'',
'',
'',
'',
'',
'B. Platelet count',
'',
'',
'',
'',
'',
'C. Triglyceride level',
'',
'',
'',
'',
'',
'D. LDL cholesterol level',
'',
'',
'',
'',
''), ncol = 3, nrow = 8,
label_x = 0.01, hjust = 0,
label_size = 28, scale = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1, 0.1, 1),
label_fontfamily = "Helvetica")
grDevices::cairo_pdf("img/fig_s10_group_ind_comparisons_physical.pdf", width = 24, height = 24, onefile = T)
grid.arrange(arrangeGrob(fig_s10,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s11_group_ind_comparisons_wbc.pdf", width = 24, height = 24, onefile = T)
grid.arrange(arrangeGrob(fig_s11,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s12_group_ind_comparisons_rbc.pdf", width = 24, height = 18, onefile = T)
grid.arrange(arrangeGrob(fig_s12,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()
grDevices::cairo_pdf("img/fig_s13_group_ind_comparisons_other.pdf", width = 24, height = 24, onefile = T)
grid.arrange(arrangeGrob(fig_s13,
bottom = textGrob("Genetic distance from the GWAS sample",
gp=gpar(fontfamily = "Helvetica", fontsize=24))))
dev.off()