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Copy pathHomocysteine_workup.R
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Homocysteine_workup.R
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load("Vitacog_data.rda")
expanded_pheno = expanded_pheno = haven::read_sav("VITACOG_FA_merged.sav")
source("~/Documents/Elysium/Customer Bioinformatic Pipeline/blood_sysAgesFunc.R")
library(glmnet)
library(data.table)
library(ggpubr)
library(dplyr)
## ==============================================
# Data setup
## ==============================================
pheno_compare = pheno
age_diff = data.frame(patient = expanded_pheno$VCID,
time_diff = as.numeric(difftime(expanded_pheno$Date_Ep2, expanded_pheno$Date_ep1)/365))
age_diff = age_diff[age_diff$patient %in% pheno_compare$id,]
age_diff = age_diff[match(pheno_compare[pheno_compare$visit == "F", id], age_diff$patient), ]
pheno_compare[pheno_compare$visit == "F","age"] = pheno_compare[pheno_compare$visit == "F","age"] + age_diff$time_diff
pheno_compareR = pheno_compare[pheno_compare$visit == "R",]
pheno_compareF = pheno_compare[pheno_compare$visit == "F",]
cog_scoresR = expanded_pheno[expanded_pheno$VCID %in% pheno_compareR$id, c("VCID",
# "Clox_score",
"Category_fluency",
"HVLT_TR",
"HVLT_DR",
"SRM",
"Graded_naming",
"Trail_Making_A",
"Trail_Making_B",
"SDMT",
"Map_Search",
"MMSE")]
cog_scoresF = expanded_pheno[expanded_pheno$VCID %in% pheno_compareF$id, c("VCID",
# "Clox_score_2",
"Category_fluency_2",
"HVLT_TR_2",
"HVLT_DR_2",
"SRM_2",
"Graded_naming_2",
"Trail_Making_A_2",
"Trail_Making_B_2",
"SDMT_2",
"Map_Search_2",
"MMSE_2")]
colnames(cog_scoresF) = c("VCID",
# "Clox_score",
"Category_fluency",
"HVLT_TR",
"HVLT_DR",
"SRM",
"Graded_naming",
"Trail_Making_A",
"Trail_Making_B",
"SDMT",
"Map_Search",
"MMSE")
pheno_compareR = left_join(pheno_compareR, cog_scoresR, by = c("id" = "VCID"))
pheno_compareF = left_join(pheno_compareF, cog_scoresF, by = c("id" = "VCID"))
pheno_compare = rbind(pheno_compareR, pheno_compareF)
CpG_data_compare = CpG_data
rm(pheno, CpG_data, expanded_pheno, age_diff, cog_scoresF, cog_scoresR, pheno_compareF, pheno_compareR)
## ==============================================
# Get blood systems ages
## ==============================================
Blood_ages = calculate_index_2_signatures_blood(CpG_data_compare, pheno_compare$age, 50, "batch1")
colnames(Blood_ages) = paste(colnames(Blood_ages), "B", sep = "")
Blood_roa = Blood_ages/pheno_compare$age
colnames(Blood_roa) = paste(colnames(Blood_roa), "roa", sep = "_")
Blood_res.age = Blood_ages - pheno_compare$age
colnames(Blood_res.age) = paste(colnames(Blood_res.age), "res", sep = "_")
pheno_compare = as.data.frame(cbind(pheno_compare, Blood_roa, Blood_res.age))
rownames(pheno_compare) = pheno_compare$unique.id
rm(Blood_ages, Blood_res.age, Blood_roa)
## ==========
# IndexB and other Clocks
## ==========
IndexB_others = calcDNAmClocks(pheno_compare, t(CpG_data_compare))
IndexB_others = IndexB_others[["targets"]][,c((ncol(IndexB_others[["targets"]])-8):(ncol(IndexB_others[["targets"]])-2))]
IndexB_roa = IndexB_others/pheno_compare$age
colnames(IndexB_roa) = paste(colnames(IndexB_roa), "roa", sep = "_")
IndexB_res.age = IndexB_others-pheno_compare$age
colnames(IndexB_res.age) = paste(colnames(IndexB_res.age), "res", sep = "_")
pheno_compare = cbind(pheno_compare, IndexB_others, IndexB_roa, IndexB_res.age)
rm(IndexB_others, IndexB_res.age, IndexB_roa)
## ==========
# DunedinPACE clock
## ==========
source("~/Documents/Elysium/dunedinPACE_fxn.R")
DunedinPace_BAs = as.data.frame(calc_Dunedin_PACE(t(CpG_data_compare), prop_decrease = 0.1))
colnames(DunedinPace_BAs) = "DunedinPACE"
pheno_compare = cbind(pheno_compare, DunedinPace_BAs)
rm(DunedinPace_BAs)
colnames(pheno_compare) = gsub("DNAm","",colnames(pheno_compare))
colnames(pheno_compare) = gsub("_roa",".RoA",colnames(pheno_compare))
colnames(pheno_compare) = gsub("Zhang2019en","Zhang2019",colnames(pheno_compare))
## --- Check correlations of clock's RoAs
psych::pairs.panels(pheno_compare[,c(67:73, 81)],
method = "pearson",
lm = T,
ci = T,
density = T,
ellipses = F,
hist.col = "lightsteelblue",
stars = T)
## ==========
# Delta dataset (T2 - T1)
## ==========
pheno_F = as.data.frame(subset(pheno_compare[order(pheno_compare$id),], visit == "F"))
pheno_R = as.data.frame(subset(pheno_compare[order(pheno_compare$id),], visit == "R" & id %in% pheno_F$id))
pheno_delta = pheno_F[,-c(1,3,8)]
cols_of_interest = colnames(pheno_delta[,c(11, 26, 29:56, 64:78)])
pheno_delta[,cols_of_interest] = pheno_F[,cols_of_interest] - pheno_R[,cols_of_interest]
rm(pheno_F, pheno_R, cols_of_interest)
## ==========
# Remove outlier patient (optional)
## ==========
#pheno_delta = pheno_delta[!pheno_delta$id == "A080",]
#############################################################
# BEGIN PLOTTING
#############################################################
## =======================================
# IndexB_roa (baseline) ~ tHcy regressions (baseline)
## =======================================
library(ggpubr)
library(MASS)
library(ggpmisc)
scatter_dataset = na.omit(pheno_compare[, c("id", "Horvath2013.RoA",
"Hannum2013.RoA",
"Weidner2014.RoA",
"Horvath2018.RoA",
"Zhang2019.RoA",
"PhenoAge.RoA",
"IndexB.RoA",
"DunedinPACE",
"tHcy",
"visit"#,
# "atrophyRate_year"
)])
scatterR = data.table::setDT(scatter_dataset[scatter_dataset$visit == "R",])
scatterR = scatterR[order(scatterR$id),]
scatter_final = scatterR[,-"visit"]
colnames(scatter_final) = c("id", "Horvath (2013)", "Hannum (2013)", "Weidner (2014)", "Horvath (2018)", "Zhang (2019)", "DNAmPhenoAge (2018)", "Index",
"DunedinPACE (2022)",
"tHcy")
scatter_final = data.table::melt(scatter_final, id.vars = c("id", "tHcy"), variable.name = "clock")
#scatter_final = data.table::melt(scatter_final, id.vars = c("id", "tHcy", "atrophyRate_year"), variable.name = "clock")
rm(scatter_dataset, scatterF, scatterR)
# Adjust clock factor order
scatter_final$clock = factor(scatter_final$clock, levels = c("Index",
"DunedinPACE (2022)",
"DNAmPhenoAge (2018)", "Hannum (2013)", "Horvath (2013)", "Horvath (2018)", "Weidner (2014)", "Zhang (2019)"))
plt1 = ggplot(scatter_final,
aes(x=tHcy,
y=value,
group=clock#,
# color=atrophyRate_year
)) +
geom_point() +
geom_smooth(method = "lm", color = "red", fill = "lightgrey") +
theme_bw() +
theme(legend.position = "right", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
legend.text = element_text(size = 9),
legend.title = element_text(size = 9),
strip.text.x = element_text(size = 12)) +
labs(#title = "Clocks ~ tHcy",
#subtitle = "All baseline",
x = "Baseline tHcy",
y = "Rate of Aging (ROA)"
) +
facet_wrap(vars(clock)) +
stat_cor(method = "pearson", cor.coef.name = expression(italic("r")),
label.x = -Inf, label.y = Inf, hjust = -0.08, vjust = 2.4) +
stat_regline_equation(use_label(c("eq", "R2")), label.x = -Inf, label.y = Inf, hjust = -0.05, vjust = 1.13)
# scale_color_gradient2(name = "Atrophy Rate/yr", low = "turquoise", mid = "blue", high = "red", midpoint = 1)
plt1
## ----------------------
# Revised version with
# letter annotations
## ----------------------
library(ggpubr)
library(lemon)
library(patchwork)
library(grid)
library(ggpmisc)
plt1.1 = list()
letter_counter = 1
for (i in levels(scatter_final$clock)) {
var_name = i
plt1.1[[i]] =ggscatter(scatter_final[scatter_final$clock==i,],
x = "tHcy",
y = "value",
facet.by = "clock",
title = LETTERS[letter_counter],
alpha = 0.3,
add = "reg.line",
conf.int = T,
add.params = list(color = "blue", fill = "grey")) +
theme_bw() +
theme(legend.position = "none",
plot.title = element_text(size = 14),
text = element_text(size = 14),
#axis.text = element_text(size = 12),
#strip.text.x = element_text(size = 12),
#axis.title = element_blank(),
) +
stat_cor(method = "pearson", cor.coef.name = expression(italic("r")),
label.x = -Inf, label.y = Inf, hjust = -0.08, vjust = 2.4) +
stat_regline_equation(use_label(c("eq", "R2")), label.x = -Inf, label.y = Inf, hjust = -0.05, vjust = 1.13) +
xlab("Baseline tHcy") + ylab("Rate of Aging (ROA)")
letter_counter = letter_counter+1
}
plt1.1[["blank_plot"]] = ggplot(scatter_final) + theme_minimal() +
theme(legend.position = "none", text = element_text(size = 14),
plot.title = element_blank()) +
xlab("Baseline tHcy") + ylab("Rate of Aging (ROA)")
wrap_plots(plt1.1, nrow = 3) + plot_layout(axis_titles = 'collect')
ggsave(filename = "Figure_1.png", path = "~/Google Drive/My Drive/Vitacog paper workup data/",
width = 9, height = 7.5, units = "in", dpi = 600)
rm(scatter_final, plt1, plt1.1, i, xlab1, ylab1, letter_counter, blank_plot, var_name)
#xlab1 <- plt1$labels$x
#ylab1 <- plt1$labels$y
#wrap_plots(plt1.1[1:8], nrow = 3) + guide_area() + plot_layout(guides = "collect") +
# plot_annotation(tag_levels = 'A') & theme(plot.tag.position = "topleft",
# plot.margin = unit(c(0.04,0.04,0.5,0.5), "cm"))
#grid::grid.draw(grid::textGrob(xlab1, x = unit(0.55, "npc"), y = unit(0.03, "npc")))
#grid::grid.draw(grid::textGrob(ylab1, x = unit(0.02, "npc"), y = unit(0.55, "npc"), rot = 90))
## =======================================
# Plotting IndexB + Clocks (with threshold & w/o threshold)
## =======================================
library(data.table)
library(gtable)
tHcy_thresh = 13
## === ROA plots
pheno_delta_roa = pheno_delta[,c(1,7,26,64:70,
78)] # DunedinPACE is last one
subset_list = subset(pheno_compare$id, pheno_compare$tHcy >=tHcy_thresh & pheno_compare$visit == "R")
colnames(pheno_delta_roa) = c("id", "treatment", "tHcy", "Horvath (2013)", "Hannum (2013)", "Weidner (2014)", "Horvath (2018)", "Zhang (2019)", "DNAmPhenoAge (2018)", "Index",
"DunedinPACE (2022)"
)
pheno_delta_roa = melt(pheno_delta_roa, id.vars = c("id","treatment","tHcy"))
# Adjust clock factor order
pheno_delta_roa$variable = factor(pheno_delta_roa$variable, levels = c("Index",
"DunedinPACE (2022)",
"DNAmPhenoAge (2018)", "Hannum (2013)", "Horvath (2013)", "Horvath (2018)", "Weidner (2014)", "Zhang (2019)"))
pheno_delta_roa$treatment = ifelse(pheno_delta_roa$treatment == "active", "B-vitamin complex", "Placebo")
pheno_delta_roa$treatment = factor(pheno_delta_roa$treatment, levels = c("Placebo", "B-vitamin complex"))
library(ggpubr)
library(lemon)
library(patchwork)
library(grid)
# WITH threshold
plt1 = ggboxplot(subset(pheno_delta_roa, pheno_delta_roa$id %in% subset_list),
x = "treatment",
y = "value",
color = "treatment",
add = "jitter",
facet.by = "variable",
scales = "free",
# title = expression(paste0("VITACOG Clock 2-Year Rate of Aging ", Delta)),
subtitle = paste0("tHcy >=",
tHcy_thresh,
" umol/L - Placebo = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$id %in% subset_list &
pheno_delta_roa$variable == "Index" &
pheno_delta_roa$treatment == "Placebo")),
", B-vitamin complex = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$id %in% subset_list &
pheno_delta_roa$variable == "Index" &
pheno_delta_roa$treatment == "B-vitamin complex"))),
xlab = FALSE) +
stat_compare_means(method = "t.test",
aes(label = paste0("p = ", after_stat(p.format))),
label.x.npc = 0.33) +
ylab("2-Year change in ROA") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
theme_bw() +
# theme(legend.position = "none") +
ggsci::scale_color_jco() +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
legend.key.size = unit(2, "cm"))
plt1 <- ggplot_build(plt1)
plt1 <- reposition_legend(ggplot_gtable(plt1), "center",
legend = g_legend(ggplot_gtable(plt1)),
panel = "panel-3-3")
legend_grob <- which(sapply(plt1$grobs, function(x) x$name) == "guide-box")
plt1$grobs[[legend_grob]] <- zeroGrob()
plt1 <- gtable_add_padding(plt1, unit(c(0,-.15, 0, 0), "npc"))
grid.newpage()
grid.draw(plt1)
plt1.1 = list()
for (i in levels(pheno_delta_roa$variable)) {
plt1.1[[i]] = ggboxplot(pheno_delta_roa[pheno_delta_roa$id %in% subset_list & pheno_delta_roa$variable == i,],
x = "treatment",
y = "value",
color = "treatment",
add = "jitter",
facet.by = "variable",
scales = "free",
# title = expression(paste0("VITACOG Clock 2-Year Rate of Aging ", Delta)),
subtitle = paste0("tHcy >=",
tHcy_thresh,
" umol/L - Placebo = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$id %in% subset_list &
pheno_delta_roa$variable == "Index" &
pheno_delta_roa$treatment == "Placebo")),
", B-vitamin complex = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$id %in% subset_list &
pheno_delta_roa$variable == "Index" &
pheno_delta_roa$treatment == "B-vitamin complex"))),
xlab = FALSE) +
stat_compare_means(method = "t.test",
aes(label = paste0("p = ", after_stat(p.format))),
label.x.npc = 0.33) +
ylab("2-Year change in ROA") +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
theme_bw() +
# theme(legend.position = "none") +
ggsci::scale_color_jco() +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
legend.key.size = unit(2, "cm"),
plot.margin = unit(c(0,0,0,0), "cm"))
title.lab <- plt1.1[[i]]$labels$subtitle
plt1.1[[i]]$labels$subtitle <- " "
if (i %in% c("DunedinPACE (2022)", "DNAmPhenoAge (2018)", "Horvath (2013)",
"Horvath (2018)", "Zhang (2019)")) {
plt1.1[[i]]$labels$y <- " "
}
}
wrap_plots(plt1.1, nrow = 3) + guide_area() + plot_layout(guides = "collect") +
plot_annotation(tag_levels = 'A') & theme(plot.tag.position = "topleft",
plot.margin = unit(c(0.4,0.04,0.04,0.04), "cm"),
legend.position = "right")
grid::grid.draw(grid::textGrob(title.lab, x = unit(0.44, "npc"), y = unit(0.98, "npc")))
# WITHOUT threshold
plt2 = ggboxplot(pheno_delta_roa,
x = "treatment",
y = "value",
color = "treatment",
add = "jitter",
facet.by = "variable",
scales = "free",
# title = expression(paste("VITACOG Clock Rate of Aging ", Delta, " - no tHcy threshold")),
subtitle = paste0("Placebo = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$variable == "IndexB_roa" &
pheno_delta_roa$treatment == "placebo")),
", Active = ",
nrow(subset(pheno_delta_roa, pheno_delta_roa$variable == "IndexB_roa" &
pheno_delta_roa$treatment == "active"))),
xlab = FALSE) +
stat_compare_means(method = "t.test") +
ylab(expression(paste("Clock Rate of Aging ", Delta))) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
theme_bw() +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank())
rm(pheno_delta_roa, subset_list, tHcy_thresh, legend_grob, plt1, plt1.1, plt2, i, title.lab)
## =======================================
#Significance sliding window for baseline homocysteine thresholds vs rate of aging deltas (all blood clocks)
## =======================================
library(dplyr)
library(stringr)
library(data.table)
library(dplyr)
##-----------------------------------------
# Establish windows for homocysteine levels
##-----------------------------------------
thresh.levels = seq(from = 5.5, to = 31, by = 0.5)
window_results = list()
##-----------------------------------------
# loop through all homocysteine windows and record threshold and p-value for Wilcoxon test
##-----------------------------------------
for (i in thresh.levels) {
inclusion = pheno_compare[pheno_compare$visit == "R" & pheno_compare$tHcy >= i, "id"]
pheno.high_tHcy = subset(pheno_delta, pheno_delta$id %in% inclusion)
# Seperate out placebo vs active in year2
pheno.high_tHcy_pl = subset(pheno.high_tHcy, pheno.high_tHcy$treatment == "placebo")
pheno.high_tHcy_ac = subset(pheno.high_tHcy, pheno.high_tHcy$treatment == "active")
rm(pheno.high_tHcy)
if (nrow(pheno.high_tHcy_ac) < 2 | nrow(pheno.high_tHcy_pl) < 2) {break}
# collect relationships
output = data_frame(Horvath2013.RoA = t.test(pheno.high_tHcy_pl$Horvath2013.RoA, pheno.high_tHcy_ac$Horvath2013.RoA)[["p.value"]],
Hannum2013.RoA = t.test(pheno.high_tHcy_pl$Hannum2013.RoA, pheno.high_tHcy_ac$Hannum2013.RoA)[["p.value"]],
Weidner2014.RoA = t.test(pheno.high_tHcy_pl$Weidner2014.RoA, pheno.high_tHcy_ac$Weidner2014.RoA)[["p.value"]],
Horvath2018.RoA = t.test(pheno.high_tHcy_pl$Horvath2018.RoA, pheno.high_tHcy_ac$Horvath2018.RoA)[["p.value"]],
Zhang2019.RoA = t.test(pheno.high_tHcy_pl$Zhang2019.RoA, pheno.high_tHcy_ac$Zhang2019.RoA)[["p.value"]],
PhenoAge.RoA = t.test(pheno.high_tHcy_pl$PhenoAge.RoA, pheno.high_tHcy_ac$PhenoAge.RoA)[["p.value"]],
DunedinPACE = t.test(pheno.high_tHcy_pl$DunedinPACE, pheno.high_tHcy_ac$DunedinPACE)[["p.value"]],
IndexB.RoA = t.test(pheno.high_tHcy_pl$IndexB.RoA, pheno.high_tHcy_ac$IndexB.RoA)[["p.value"]])
window_results[[paste(i)]] = output
}
rm(output)
window_results = bind_rows(window_results, .id = "tHcy Threshold")
colnames(window_results) = c("tHcy Threshold", "Horvath (2013)", "Hannum", "Weidner", "Horvath (2018)", "Zhang", "DNAmPhenoAge",
"DunedinPACE",
"Index")
##-----------------------------------------
# Convert data into long-form for plotting
##-----------------------------------------
window_results = data.table::melt(window_results, id.vars = "tHcy Threshold")
window_results$`tHcy Threshold` = as.numeric(as.vector(window_results$`tHcy Threshold`))
# Adjust clock factor order
window_results$variable = factor(window_results$variable, levels = c("Index",
"DunedinPACE",
"DNAmPhenoAge", "Hannum", "Horvath (2013)", "Horvath (2018)", "Weidner", "Zhang"))
##-----------------------------------------
# Plot p-values for each
##-----------------------------------------
library(ggpubr)
ggplot(window_results,
aes(x=`tHcy Threshold`,
y=value,
group=variable,
color=variable)) +
geom_line() + geom_point() +
geom_hline(yintercept = 0.05, linetype = "dashed", color = "red") +
geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE, color = "lightsteelblue") +
scale_x_continuous(breaks = seq(min(window_results$`tHcy Threshold`), max(window_results$`tHcy Threshold`), by = 1)) +
theme_bw() +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.x = element_text(angle = 90, hjust=1),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
labs(title = expression(paste("VITACOG Clocks Rate of Aging ", Delta, " tHcy threshold significance")),
x = "tHcy Threshold", y="p-value") +
facet_wrap(vars(variable), scales = "free")
#date = stringr::str_replace_all(Sys.Date(), "-", "")
#fname = "VITACOG_RoA_homocysteine_thresholds"
#pdf(file = sprintf("%s_%s.pdf", date, fname), width = 16)
#print(p)
#dev.off()
rm(pheno.high_tHcy_pl, pheno.high_tHcy_ac, window_results, inclusion, thresh.levels, systems, i)
## =======================================
# IndexB_roa Delta ~ tHcy regressions (baseline)
## =======================================
library(ggpubr)
library(MASS)
scatter_dataset = na.omit(pheno_compare[, c("id", "IndexB.RoA", "tHcy", "treatment", "visit")])
scatterF = scatter_dataset[scatter_dataset$visit == "F",]
scatterR = scatter_dataset[scatter_dataset$visit == "R" & scatter_dataset$id %in% scatterF$id,]
scatterR = scatterR[order(scatterR$id),]
scatterF = scatterF[order(scatterF$id),]
scatter_final = scatterR[,-5]
scatter_final[,2] = scatterF[,2] - scatterR[,2]
rm(scatter_dataset, scatterF, scatterR)
# Change treatment levels and order
scatter_final[scatter_final$treatment == "active",4] = "B-vitamin complex"
scatter_final$treatment = factor(scatter_final$treatment, levels = c("placebo", "B-vitamin complex"))
scatter_final$hyper = factor(ifelse(scatter_final$tHcy >=15, "yes", "no"), levels = c("yes", "no"))
main.labs = c("Placebo", "B-vitamin complex")
names(main.labs) = c("placebo", "B-vitamin complex")
plt1 = ggplot(scatter_final,
aes(x=tHcy,
y=IndexB.RoA,
color=ifelse(tHcy>=15, treatment, "black"),
group=treatment
)) +
geom_point(aes(shape = as.factor(ifelse(tHcy>=15, treatment, 9)))) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
# theme(legend.position = "right", text = element_text(size = 14),
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
# legend.text = element_text(size = 9),
# legend.title = element_text(size = 9),
strip.text.x = element_text(size = 12)) +
# labs(title = expression("Index 2-year ROA"*Delta~"~ tHcy")#,
#subtitle = expression(Delta~"= T1 - T2")
# ) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = c("#0073C2FF", "#EFC000FF", "#EFC000FF", "black", "#0073C2FF")) +
stat_cor(method = "pearson", show.legend = F, label.x.npc = "left", label.y = 0.09) +
xlab("Baseline tHcy") + ylab(expression("Index 2-Year ROA"*Delta))# +
# ylab(expression("BraiNorm"~Delta)) +
# scale_color_gradient2(name = expression("tHcy"~Delta), low = "turquoise", mid = "blue", high = "red", midpoint = -5)
# scale_color_gradient2(name = "Atrophy Rate/yr", low = "turquoise", mid = "blue", high = "red", midpoint = 1)
plt1.1 = ggplot(scatter_final[scatter_final$treatment=="placebo",],
aes(x=tHcy,
y=IndexB.RoA,
color=ifelse(tHcy>=15, treatment, "black"),
group=treatment
)) +
geom_point(aes(shape = hyper)) +
scale_shape_manual(values = c(16, 15)) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = c("#0073C2FF", "#0073C2FF","black")) +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson", cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt1)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt1)$layout$panel_scales_y[[1]]$range$range)
plt1.2 = ggplot(scatter_final[scatter_final$treatment=="B-vitamin complex",],
aes(x=tHcy,
y=IndexB.RoA,
color=ifelse(tHcy>=15, treatment, "black"),
group=treatment
)) +
geom_point(aes(shape = hyper)) +
scale_shape_manual(values = c(17, 15)) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = c("#EFC000FF", "#EFC000FF", "black")) +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson", cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt1)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt1)$layout$panel_scales_y[[1]]$range$range)
plt1.2$labels$y <- " "
xlab1 <- plt1.1$labels$x
plt1.1$labels$x <- plt1.2$labels$x <- " "
rm(scatter_final)
## =======================================
# IndexB_roa Delta ~ tHcy (baseline >= 15) regressions
## =======================================
library(ggpubr)
library(MASS)
scatter_dataset = na.omit(pheno_compare[, c("id", "IndexB.RoA", "tHcy", "treatment", "visit")])
scatterF = scatter_dataset[scatter_dataset$visit == "F",]
scatterR = scatter_dataset[scatter_dataset$visit == "R" & scatter_dataset$id %in% scatterF$id,]
scatterR = scatterR[order(scatterR$id),]
scatterF = scatterF[order(scatterF$id),]
scatter_final = scatterR[,-5]
scatter_final[,2] = scatterF[,2] - scatterR[,2]
scatter_final = scatter_final[scatter_final$tHcy >= 15,]
rm(scatter_dataset, scatterF, scatterR)
# Change treatment levels and order
scatter_final[scatter_final$treatment == "active",4] = "B-vitamin complex"
scatter_final$treatment = factor(scatter_final$treatment, levels = c("placebo", "B-vitamin complex"))
main.labs = c("Placebo", "B-vitamin complex")
names(main.labs) = c("placebo", "B-vitamin complex")
plt2 = ggplot(scatter_final,
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(aes(shape = treatment, color = treatment)) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
# theme(legend.position = "right", text = element_text(size = 14),
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
# legend.text = element_text(size = 9),
# legend.title = element_text(size = 9),
strip.text.x = element_text(size = 12)) +
# labs(title = expression("IndexB ROA"*Delta~"~ tHcy")#,
#subtitle = expression(Delta~"= T1 - T2")
# ) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
stat_cor(method = "pearson", show.legend = F, label.x.npc = "left", label.y = 0.09) +
#xlab(expression(paste("IndexB_roa ", Delta))) +
ggsci::scale_color_jco() +
xlab("Baseline tHcy (>=15)") +
ylab(expression("Index 2-Year ROA"*Delta))# +
# ylab(expression("BraiNorm"~Delta)) +
# scale_color_gradient2(name = expression("tHcy"~Delta), low = "turquoise", mid = "blue", high = "red", midpoint = -5)
# scale_color_gradient2(name = "Atrophy Rate/yr", low = "turquoise", mid = "blue", high = "red", midpoint = 1)
plt2.1 = ggplot(scatter_final[scatter_final$treatment=="placebo",],
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(aes(shape = treatment, color = treatment)) + scale_shape_manual(values = 16) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = c("#0073C2FF", "#0073C2FF")) +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson", cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy (>=15)") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt2)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt2)$layout$panel_scales_y[[1]]$range$range)
plt2.2 = ggplot(scatter_final[scatter_final$treatment=="B-vitamin complex",],
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(aes(shape = treatment, color = treatment)) + scale_shape_manual(values = 17) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = c("#EFC000FF", "#EFC000FF")) +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson", cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy (>=15)") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt2)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt2)$layout$panel_scales_y[[1]]$range$range)
plt2.2$labels$y <- " "
xlab2 <- plt2.1$labels$x
plt2.1$labels$x <- plt2.2$labels$x <- " "
rm(scatter_final)
## =======================================
# IndexB_roa Delta ~ tHcy (baseline <= 15) regressions
## =======================================
library(ggpubr)
library(MASS)
scatter_dataset = na.omit(pheno_compare[, c("id", "IndexB.RoA", "tHcy", "treatment", "visit")])
scatterF = scatter_dataset[scatter_dataset$visit == "F",]
scatterR = scatter_dataset[scatter_dataset$visit == "R" & scatter_dataset$id %in% scatterF$id,]
scatterR = scatterR[order(scatterR$id),]
scatterF = scatterF[order(scatterF$id),]
scatter_final = scatterR[,-5]
scatter_final[,2] = scatterF[,2] - scatterR[,2]
scatter_final = scatter_final[scatter_final$tHcy <= 15,]
rm(scatter_dataset, scatterF, scatterR)
# Change treatment levels and order
scatter_final[scatter_final$treatment == "active",4] = "B-vitamin complex"
scatter_final$treatment = factor(scatter_final$treatment, levels = c("placebo", "B-vitamin complex"))
main.labs = c("Placebo", "B-vitamin complex")
names(main.labs) = c("placebo", "B-vitamin complex")
plt3 = ggplot(scatter_final,
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(color = "black", shape = 15) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
# theme(legend.position = "right", text = element_text(size = 14),
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
# legend.text = element_text(size = 9),
# legend.title = element_text(size = 9),
strip.text.x = element_text(size = 12)) +
# labs(title = expression("IndexB ROA"*Delta~"~ tHcy")#,
#subtitle = expression(Delta~"= T1 - T2")
# ) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
stat_cor(method = "pearson", show.legend = F, label.x.npc = "left", label.y = 0.09) +
#xlab(expression(paste("IndexB_roa ", Delta))) +
ggsci::scale_color_jco() +
xlab("Baseline tHcy (<=15)") +
ylab(expression("Index 2-Year ROA"*Delta))# +
# ylab(expression("BraiNorm"~Delta)) +
# scale_color_gradient2(name = expression("tHcy"~Delta), low = "turquoise", mid = "blue", high = "red", midpoint = -5)
# scale_color_gradient2(name = "Atrophy Rate/yr", low = "turquoise", mid = "blue", high = "red", midpoint = 1)
plt3.1 = ggplot(scatter_final[scatter_final$treatment=="placebo",],
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(color = "black", shape = 15) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = "#0073C2FF") +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson", cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy (<=15)") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt3)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt3)$layout$panel_scales_y[[1]]$range$range)
plt3.2 = ggplot(scatter_final[scatter_final$treatment=="B-vitamin complex",],
aes(x=tHcy,
y=IndexB.RoA,
group=treatment
)) +
geom_point(color = "black", shape = 15) +
geom_smooth(aes(group = treatment, color = treatment), method = "rlm", fill = "lightgrey") +
theme_bw() +
coord_cartesian(ylim = c(NA, 0.1)) +
theme(legend.position = "none", text = element_text(size = 14),
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12),
strip.text.x = element_text(size = 12)) +
facet_wrap(vars(treatment), labeller = labeller(treatment = main.labs)) +
scale_color_manual(values = "#EFC000FF") +
stat_regline_equation(use_label(c("eq", "R2")), label.x.npc = "left", label.y = 0.09) +
stat_cor(method = "pearson",cor.coef.name = "r", show.legend = F, label.x.npc = "left", label.y = 0.09, vjust = 2) +
xlab("Baseline tHcy (<=15)") + ylab(expression("Index 2-Year ROA"*Delta)) +
xlim(ggplot_build(plt3)$layout$panel_scales_x[[1]]$range$range) +
ylim(ggplot_build(plt3)$layout$panel_scales_y[[1]]$range$range)
plt3.2$labels$y <- " "
xlab3 <- plt3.1$labels$x
plt3.1$labels$x <- plt3.2$labels$x <- " "
rm(scatter_final)
(plt1.1 + plt1.2)/
(plt2.1 + plt2.2)/
(plt3.1 + plt3.2) + plot_annotation(tag_levels = 'A') & theme(plot.tag.position = c(0,1),
plot.margin = unit(c(0.3,0.3,0.3,0.3), "cm"))
grid::grid.draw(grid::textGrob(xlab1, x = unit(0.55, "npc"), y = unit(0.68, "npc")))
grid::grid.draw(grid::textGrob(xlab2, x = unit(0.55, "npc"), y = unit(0.36, "npc")))
grid::grid.draw(grid::textGrob(xlab3, x = unit(0.55, "npc"), y = unit(0.04, "npc")))
rm(window_results, scatter_final, main.labs, xlab1, xlab2, xlab3)
rm(list = grep("plt", ls(), value = T))
## =======================================
# IndexB_roa Delta ~ baseline tHcy SLOPES through thresholds
## =======================================
library(ggpubr)
library(MASS)
library(dplyr)
scatter_dataset = na.omit(pheno_compare[, c("id", "IndexB.RoA", "tHcy", "treatment", "visit")])
scatterF = scatter_dataset[scatter_dataset$visit == "F",]
scatterR = scatter_dataset[scatter_dataset$visit == "R" & scatter_dataset$id %in% scatterF$id,]
scatterR = scatterR[order(scatterR$id),]
scatterF = scatterF[order(scatterF$id),]
scatter_final = scatterR[,-5]
scatter_final[,2] = scatterF[,2] - scatterR[,2]
rm(scatter_dataset, scatterF, scatterR)
##-----------------------------------------
# Establish windows for homocysteine levels
##-----------------------------------------
thresh.levels = seq(from = 5.5, to = 31, by = 0.5)
window_results = list()
effect.size = list()
##-----------------------------------------
# loop through all homocysteine windows and record threshold and p-value for t-test
##-----------------------------------------
for (i in thresh.levels) {
pheno.high_tHcy = scatter_final[scatter_final$tHcy >= i, ]
# Seperate out placebo vs active in year2
pheno.high_tHcy_pl = subset(pheno.high_tHcy, pheno.high_tHcy$treatment == "placebo")
pheno.high_tHcy_ac = subset(pheno.high_tHcy, pheno.high_tHcy$treatment == "active")
rm(pheno.high_tHcy)
if (nrow(pheno.high_tHcy_ac) < 3 | nrow(pheno.high_tHcy_pl) < 3) {break}
# collect relationships
placebo = summary(rlm(pheno.high_tHcy_pl$IndexB.RoA ~ pheno.high_tHcy_pl$tHcy))
active = summary(rlm(pheno.high_tHcy_ac$IndexB.RoA ~ pheno.high_tHcy_ac$tHcy))
placebo.cor = cor(pheno.high_tHcy_pl$IndexB.RoA,pheno.high_tHcy_pl$tHcy)
active.cor = cor(pheno.high_tHcy_ac$IndexB.RoA,pheno.high_tHcy_ac$tHcy)
output = data.frame(Pl_slope = placebo[["coefficients"]][2],
Pl_std.err = placebo[["coefficients"]][4],
Ac_slope = active[["coefficients"]][2],
Ac_std.err = active[["coefficients"]][4],
Pl_cor = placebo.cor,
Ac_cor = active.cor
)
window_results[[paste(i)]] = output
effect.size[[paste(i)]] = effectsize::cohens_d(pheno.high_tHcy_ac$IndexB.RoA, pheno.high_tHcy_pl$IndexB.RoA,
adjust = T, # IF TRUE WILL USE HEDGES D
paired = F)
}
rm(output, placebo, active, i, thresh.levels, pheno.high_tHcy_pl, pheno.high_tHcy_ac, scatter_final, active.cor, placebo.cor)
window_results = bind_rows(window_results, .id = "tHcy Threshold")
effect.size = bind_rows(effect.size, .id = "tHcy Threshold")
##-----------------------------------------
# Convert data into long-form for plotting
##-----------------------------------------
window_results_pl = window_results[,c(1:3,6)]
window_results_pl$treatment = rep("Placebo", nrow(window_results_pl))
colnames(window_results_pl) = c("tHcy Threshold","slope","std.err","correlation","treatment")
window_results_ac = window_results[,c(1,4,5,7)]
window_results_ac$treatment = rep("Active", nrow(window_results_ac))
colnames(window_results_ac) = c("tHcy Threshold","slope","std.err","correlation","treatment")
window_results = rbind(window_results_pl, window_results_ac)
window_results$`tHcy Threshold` = as.numeric(as.vector(window_results$`tHcy Threshold`))
rm(window_results_ac, window_results_pl)
window_results[window_results$treatment == "Active","treatment"] = "B-vitamin complex"
window_results$treatment = factor(window_results$treatment, levels = c("Placebo", "B-vitamin complex"))
effect.size$`tHcy Threshold` = as.numeric(as.vector(effect.size$`tHcy Threshold`))
effect.size$abs.effect = abs(effect.size[[2]])
##-----------------------------------------
# Plot slopes for each
##-----------------------------------------
library(ggpubr)
plt4 = ggplot(window_results,
aes(x=`tHcy Threshold`,
y=slope,
group=treatment,
color=treatment)) +
geom_line() + geom_point() + #geom_vline(xintercept = 13, color = "red", linetype = 2) +
geom_ribbon(aes(ymin = slope-std.err, ymax = slope+std.err), alpha = 0.1, colour = NA) +
#geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = F, color = "lightblue") +
scale_x_continuous(breaks = seq(min(window_results$`tHcy Threshold`), max(window_results$`tHcy Threshold`), by = 1)) +
ggsci::scale_color_jco() +
theme_bw() +
ylab(expression(beta[1])) +
theme(text = element_text(size = 14),
# legend.position = "none",
# legend.position = c(0.125, 0.18), # Coordinates with just p-value plot
legend.position = c(0.1, 0.3), # Coordinates with added effect plot
axis.text.y = element_text(size = 12),
plot.title = element_text(size=15),
axis.title = element_text(size = 12)) #+
plt4.1 = ggplot(window_results,
aes(x=`tHcy Threshold`,
y=correlation,
group=treatment,
color=treatment)) +
#geom_errorbar(aes(ymin = slope-std.err, ymax = slope+std.err), width = 0.1) +
geom_line() + geom_point()+
scale_x_continuous(breaks = seq(min(window_results$`tHcy Threshold`), max(window_results$`tHcy Threshold`), by = 1)) +
ggsci::scale_color_jco() +
theme_bw() +
theme(legend.position = "none", text = element_text(size = 14),
# axis.text.x = element_text(angle = 90, hjust=1),
axis.text.y = element_text(size = 12),