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Horvath_DNAmage_FactorAssociation.R
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setwd("/Users/jrca253/Documents/EpigeneticAge/test_code")
library(ggplot2)
library(moments)
rm(list=ls()); gc();
model.dir <- "HorvathClock/"
result.file.K <- paste(model.dir, "ScenarioResults.csv", sep = "")
cov.file.K <- paste(model.dir, "cov_K.txt", sep = "")
###########################################################################################
# LOAD DNAm AGES AND SAMPLE AGES
###########################################################################################
##### DNAm AGES ####
scenarios <- read.csv( result.file.K )
result.K <- as.numeric( scenarios$DNAmAge.21k.Imputed.Normalized )
##### SAMPLE AGES #####
df.cov.K <- read.table(cov.file.K, header = TRUE, row.names = 1, sep = '\t')
sample.ages.K <- as.numeric(as.vector(df.cov.K["Age",]))
residual.K <- result.K - sample.ages.K
##### Add result and residual to cov dataframe #####
df.cov.K["DNAm.Age",] <- result.K
df.cov.K["DNAm.Age.Residual",] <- residual.K
for (i in 2:dim(df.cov.K)[[2]] ){
df.cov.K[,i]=as.numeric(as.character(df.cov.K[,i]))
}
##########################################################################
# Multivariate Correlation Analysis
##########################################################################
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age",]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual",]))
Race <- as.numeric(as.vector(df.cov.K["RaceWhite",]))
BMI <- as.numeric(as.vector(df.cov.K["LocationUrban",]))
Cig.Pack.Years <- as.numeric(as.vector(df.cov.K["Cig.Pack.Years",]))
Smoking <- as.numeric(as.vector(df.cov.K["SmokingYes",]))
Drinking <- as.numeric(as.vector(df.cov.K["DrinkingYes",]))
Menarche <- as.numeric(as.vector(df.cov.K["Menarche",]))
Been.PregnantYes <- as.numeric(as.vector(df.cov.K["Been.PregnantYes",]))
Times.Pregnant <- as.numeric(as.vector(df.cov.K["Times.Pregnant",]))
Parity <- as.numeric(as.vector(df.cov.K["Parity",]))
Parity[Parity == 41] <- NA #Outlier in the data...
Age.FB <- as.numeric(as.vector(df.cov.K["Age.FB",]))
Menopause.Age <- as.numeric(as.vector(df.cov.K["Menopause.Age",]))
VDYes <- as.numeric(as.vector(df.cov.K["VDYes",]))
Pre.Menopause <- as.numeric(as.vector(df.cov.K["MenopausePre-menopausal",]))
Post.Menopause <- as.numeric(as.vector(df.cov.K["MenopausePost-menopausal",]))
Location.Urban <- as.numeric(as.vector(df.cov.K["LocationUrban",]))
Location.Rural <- as.numeric(as.vector(df.cov.K["LocationRural",]))
Menopause <- c()
Location <- c()
for(i in 1:length(Location.Rural) ){
pre <- Pre.Menopause[i]
post <- Post.Menopause[i]
rur <- Location.Rural[i]
urb <- Location.Urban[i]
post.yes <- ifelse(post == 1 && pre == 0, 1, ifelse(post == 0 && pre == 1, 0, NA) )
urban.yes <- ifelse(urb == 1 && rur == 0, 1, ifelse(urb == 0 && rur == 1, 0, NA) )
Location <- as.vector(as.vector(c( Location, urban.yes )))
Menopause <- as.vector(as.vector(c( Menopause, post.yes )))
}
Location <- as.numeric(Location)
Menopause <- as.numeric(Menopause)
lm.DNAm.Age <- lm(DNAm.Age ~
Race +
BMI +
Cig.Pack.Years +
Smoking +
Drinking +
Menarche +
Been.PregnantYes +
Times.Pregnant +
Parity +
Age.FB +
#Menopause.Age +
VDYes +
Menopause +
Location
)
lm.DNAm.Age.Residual <- lm(DNAm.Age.Residual ~
Race +
BMI +
Cig.Pack.Years +
Smoking +
Drinking +
Menarche +
Been.PregnantYes +
Times.Pregnant +
Parity +
Age.FB +
#Menopause.Age +
VDYes +
Menopause +
Location
)
##########################################################################
# Univariate Analysis - Race
##########################################################################
df.univar <- data.frame()
##### Correlate DNAm Age and Residual to Race #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", !is.na(df.cov.K["RaceWhite",]) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", !is.na(df.cov.K["RaceWhite",]) ]))
Race <- as.numeric(as.vector(df.cov.K["RaceWhite", !is.na(df.cov.K["RaceWhite",]) ]))
DNAm.Age.Rho <- cor.test(Race, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Race, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Race, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Race, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Race", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Race", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Race", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Race", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
DNAm.Age.White <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["RaceWhite",]) & df.cov.K["RaceWhite",] == 1)]))
DNAm.Age.AfrAmer <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["RaceWhite",]) & df.cov.K["RaceWhite",] == 0)]))
DNAm.Age.Residual.White <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["RaceWhite",]) & df.cov.K["RaceWhite",] == 1)]))
DNAm.Age.Residual.AfrAmer <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["RaceWhite",]) & df.cov.K["RaceWhite",] == 0)]))
race <- c("African American", "White")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.AfrAmer), mean(DNAm.Age.White)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.AfrAmer), sd(DNAm.Age.White)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.AfrAmer), mean(DNAm.Age.Residual.White)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.AfrAmer), sd(DNAm.Age.Residual.White)) )
df.tmp <- data.frame(race, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=race, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "Race") +
labs(y = "Mean DNAm Age") +
annotate("text", x = 0.75, y = 74.5, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 71.5, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.AfrAmer), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.White), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Race-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = race, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "Race") +
labs(y = "Mean DNAm Age Acceleration") +
annotate("text", x = 0.75, y = 21, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 20, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.AfrAmer), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.White), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Race-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Location
##########################################################################
##### Plot Correlations #####
Location.Rural <- as.numeric(as.vector(df.cov.K["LocationRural", (df.cov.K["LocationRural",] == 1 & !is.na(df.cov.K["LocationRural",]))]))
Loacation.Urban <- as.numeric(as.vector(df.cov.K["LocationUrban", (df.cov.K["LocationUrban",] == 1 & !is.na(df.cov.K["LocationUrban",]))]))
DNAm.Age.Rural <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["LocationRural",] == 1 & !is.na(df.cov.K["LocationRural",]))]))
DNAm.Age.Urban <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["LocationUrban",] == 1 & !is.na(df.cov.K["LocationUrban",]))]))
DNAm.Age.Residual.Rural <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["LocationRural",] == 1 & !is.na(df.cov.K["LocationRural",]))]))
DNAm.Age.Residual.Urban <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["LocationUrban",] == 1 & !is.na(df.cov.K["LocationUrban",]))]))
location <- c("Rural", "Urban")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.Rural), mean(DNAm.Age.Urban)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.Rural), sd(DNAm.Age.Urban)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.Rural), mean(DNAm.Age.Residual.Urban)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.Rural), sd(DNAm.Age.Residual.Urban)) )
##### Correlate DNAm Age and Residual to Location #####
Location.Rural[Location.Rural == 1] <- 0
DNAm.Age <- as.numeric( c(DNAm.Age.Rural, DNAm.Age.Urban) )
DNAm.Age.Residual <- as.numeric( c(DNAm.Age.Residual.Rural, DNAm.Age.Residual.Urban) )
Location <- as.numeric( c(Location.Rural, Loacation.Urban) )
DNAm.Age.Rho <- cor.test(Location, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Location, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Location, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Location, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Location", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Location", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Location", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Location", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
df.tmp <- data.frame(location, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=location, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "Location") +
labs(y = "Mean DNAm Age") +
annotate("text", x = 0.75, y = 68, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 65, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.Rural), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.Urban), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Location-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = location, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "Location") +
labs(y = "Mean DNAm Age Acceleration") +
annotate("text", x = 2.25, y = 23, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 2.25, y = 22, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.Rural), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.Urban), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Location-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - BMI
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", !is.na(df.cov.K["BMI",]) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", !is.na(df.cov.K["BMI",]) ]))
BMI <- as.numeric(as.vector(df.cov.K["BMI", !is.na(df.cov.K["BMI",]) ]))
DNAm.Age.Rho <- cor.test(BMI, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(BMI, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(BMI, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(BMI, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["BMI", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["BMI", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["BMI", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["BMI", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(BMI, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=BMI, y=DNAm.Age)) +
geom_point() +
labs(x = "BMI") +
labs(y = "DNAm Age") +
annotate("text", x = 62, y = 80, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 62, y = 78, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 62, y = 76, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/BMI-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = BMI, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "BMI") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 62, y = 50, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 62, y = 47, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 62, y = 44, label = paste("n = ", length(DNAm.Age.Residual), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/BMI-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Cigarette Pack Years
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Cig.Pack.Years",] >= 0 & !is.na(df.cov.K["Cig.Pack.Years",])) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Cig.Pack.Years",] >= 0 & !is.na(df.cov.K["Cig.Pack.Years",])) ]))
Cig.Pack.Years <- as.numeric(as.vector(df.cov.K["Cig.Pack.Years", (df.cov.K["Cig.Pack.Years",] >= 0 & !is.na(df.cov.K["Cig.Pack.Years",])) ]))
DNAm.Age.Rho <- cor.test(Cig.Pack.Years, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Cig.Pack.Years, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Cig.Pack.Years, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Cig.Pack.Years, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Cig.Pack.Years", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Cig.Pack.Years", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Cig.Pack.Years", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Cig.Pack.Years", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Cig.Pack.Years, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Cig.Pack.Years, y=DNAm.Age)) +
geom_point() +
labs(x = "Cigarette Pack Years") +
labs(y = "DNAm Age") +
annotate("text", x = 27, y = 35, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 27, y = 33, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 27, y = 31, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/CigPackYears-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Cig.Pack.Years, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Cigarette Pack Years") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 27, y = 50, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 27, y = 47, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 27, y = 44, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/CigPackYears-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Current Smoker
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", !is.na(df.cov.K["SmokingYes",]) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", !is.na(df.cov.K["SmokingYes",]) ]))
Smoking <- as.numeric(as.vector(df.cov.K["SmokingYes", !is.na(df.cov.K["SmokingYes",]) ]))
DNAm.Age.Rho <- cor.test(Smoking, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Smoking, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Smoking, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Smoking, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Smoking", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Smoking", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Smoking", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Smoking", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
DNAm.Age.SmokingYes <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["SmokingYes",]) & df.cov.K["SmokingYes",] == 1)]))
DNAm.Age.SmokingNo <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["SmokingYes",]) & df.cov.K["SmokingYes",] == 0)]))
DNAm.Age.Residual.SmokingYes <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["SmokingYes",]) & df.cov.K["SmokingYes",] == 1)]))
DNAm.Age.Residual.SmokingNo <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["SmokingYes",]) & df.cov.K["SmokingYes",] == 0)]))
smoking <- c("Current Smoker - No", "Current Smoker - Yes")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.SmokingNo), mean(DNAm.Age.SmokingYes)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.SmokingNo), sd(DNAm.Age.SmokingYes)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.SmokingNo), mean(DNAm.Age.Residual.SmokingYes)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.SmokingNo), sd(DNAm.Age.Residual.SmokingYes)) )
df.tmp <- data.frame(smoking, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=smoking, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age") +
annotate("text", x = 0.75, y = 71, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 69, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.SmokingNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.SmokingYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Smoking-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = smoking, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age Acceleration") +
annotate("text", x = 0.75, y = -4.4, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = -5.5, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.Residual.SmokingNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.Residual.SmokingYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Smoking-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Current Drinker
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", !is.na(df.cov.K["DrinkingYes",]) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", !is.na(df.cov.K["DrinkingYes",]) ]))
Drinking <- as.numeric(as.vector(df.cov.K["DrinkingYes", !is.na(df.cov.K["DrinkingYes",]) ]))
DNAm.Age.Rho <- cor.test(Drinking, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Drinking, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Drinking, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Drinking, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Drinking", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Drinking", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Drinking", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Drinking", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
DNAm.Age.DrinkingYes <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["DrinkingYes",]) & df.cov.K["DrinkingYes",] == 1)]))
DNAm.Age.DrinkingNo <- as.numeric(as.vector(df.cov.K["DNAm.Age", (!is.na(df.cov.K["DrinkingYes",]) & df.cov.K["DrinkingYes",] == 0)]))
DNAm.Age.Residual.DrinkingYes <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["DrinkingYes",]) & df.cov.K["DrinkingYes",] == 1)]))
DNAm.Age.Residual.DrinkingNo <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (!is.na(df.cov.K["DrinkingYes",]) & df.cov.K["DrinkingYes",] == 0)]))
drinking <- c("Alcohol Consumption - No", "Alcohol Consumption - Yes")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.DrinkingNo), mean(DNAm.Age.DrinkingYes)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.DrinkingNo), sd(DNAm.Age.DrinkingYes)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.DrinkingNo), mean(DNAm.Age.Residual.DrinkingYes)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.DrinkingNo), sd(DNAm.Age.Residual.DrinkingYes)) )
df.tmp <- data.frame(drinking, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=drinking, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age") +
annotate("text", x = 0.75, y = 71, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 69, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.DrinkingNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.DrinkingYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Alcohol-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = drinking, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age Acceleration") +
annotate("text", x = 0.75, y = 22, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 21, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.Residual.DrinkingNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.Residual.DrinkingYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Alcohol-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Age at Menarche
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Menarche",] >= 0 & !is.na(df.cov.K["Menarche",])) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Menarche",] >= 0 & !is.na(df.cov.K["Menarche",])) ]))
Menarche <- as.numeric(as.vector(df.cov.K["Menarche", (df.cov.K["Menarche",] >= 0 & !is.na(df.cov.K["Menarche",])) ]))
DNAm.Age.Rho <- cor.test(Menarche, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Menarche, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Menarche, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Menarche, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Menarche", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Menarche", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Menarche", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Menarche", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Menarche, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Menarche, y=DNAm.Age)) +
geom_point() +
labs(x = "Age at Menarche") +
labs(y = "DNAm Age") +
annotate("text", x = 8.5, y = 30, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 8.5, y = 28, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 8.5, y = 26, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/MenarcheAge-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Menarche, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Age at Menarche") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 16, y = 49, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 16, y = 46, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 16, y = 43, label = paste("n = ", length(DNAm.Age.Residual), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/MenarcheAge-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Have you been pregnant
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", !is.na(df.cov.K["Been.PregnantYes",]) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", !is.na(df.cov.K["Been.PregnantYes",]) ]))
Been.PregnantYes <- as.numeric(as.vector(df.cov.K["Been.PregnantYes", !is.na(df.cov.K["Been.PregnantYes",]) ]))
DNAm.Age.Rho <- cor.test(Been.PregnantYes, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Been.PregnantYes, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Been.PregnantYes, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Been.PregnantYes, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Been.PregnantYes", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Been.PregnantYes", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Been.PregnantYes", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Been.PregnantYes", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
DNAm.Age.Been.PregnantYes <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Been.PregnantYes",] == 1 & !is.na(df.cov.K["Been.PregnantYes",]))]))
DNAm.Age.Been.PregnantNo <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Been.PregnantYes",] == 0 & !is.na(df.cov.K["Been.PregnantYes",]))]))
DNAm.Age.Residual.Been.PregnantYes <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Been.PregnantYes",] == 1 & !is.na(df.cov.K["Been.PregnantYes",]))]))
DNAm.Age.Residual.Been.PregnantNo <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Been.PregnantYes",] == 0 & !is.na(df.cov.K["Been.PregnantYes",]))]))
pregnant <- c("Have you been pregnant? - No", "Have you been pregnant? - Yes")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.Been.PregnantNo), mean(DNAm.Age.Been.PregnantYes)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.Been.PregnantNo), sd(DNAm.Age.Been.PregnantYes)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.Been.PregnantNo), mean(DNAm.Age.Residual.Been.PregnantYes)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.Been.PregnantNo), sd(DNAm.Age.Residual.Been.PregnantYes)) )
df.tmp <- data.frame(pregnant, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=pregnant, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age") +
annotate("text", x = 0.75, y = 64.5, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 61.5, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.Been.PregnantNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.Been.PregnantYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/HaveYouBeenPregnant-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = pregnant, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "") +
labs(y = "Mean DNAm Age Acceleration") +
annotate("text", x = 2.25, y = 25, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 2.25, y = 24, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.Residual.Been.PregnantNo), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.Residual.Been.PregnantYes), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/HaveYouBeenPregnant-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Times Pregnant
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Times.Pregnant",] >= 0 & !is.na(df.cov.K["Times.Pregnant",])) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Times.Pregnant",] >= 0 & !is.na(df.cov.K["Times.Pregnant",])) ]))
Times.Pregnant <- as.numeric(as.vector(df.cov.K["Times.Pregnant", (df.cov.K["Times.Pregnant",] >= 0 & !is.na(df.cov.K["Times.Pregnant",])) ]))
DNAm.Age.Rho <- cor.test(Times.Pregnant, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Times.Pregnant, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Times.Pregnant, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Times.Pregnant, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Times.Pregnant", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Times.Pregnant", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Times.Pregnant", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Times.Pregnant", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Times.Pregnant, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Times.Pregnant, y=DNAm.Age)) +
geom_point() +
labs(x = "Times Pregnant") +
labs(y = "DNAm Age") +
annotate("text", x = 6, y = 36, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 6, y = 34, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 6, y = 32, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/TimesPregnant-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Times.Pregnant, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Times Pregnant") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 6, y = 51, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 6, y = 48, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 6, y = 45, label = paste("n = ", length(DNAm.Age.Residual), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/TimesPregnant-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Parity
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Parity",] >= 0 & df.cov.K["Parity",] != 41 & !is.na(df.cov.K["Parity",]) ) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Parity",] >= 0 & df.cov.K["Parity",] != 41 & !is.na(df.cov.K["Parity",])) ]))
Parity <- as.numeric(as.vector(df.cov.K["Parity", (df.cov.K["Parity",] >= 0 & df.cov.K["Parity",] != 41 & !is.na(df.cov.K["Parity",])) ]))
DNAm.Age.Rho <- cor.test(Parity, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Parity, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Parity, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Parity, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Parity", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Parity", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Parity", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Parity", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Parity, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Parity, y=DNAm.Age)) +
geom_point() +
labs(x = "Parity") +
labs(y = "DNAm Age") +
annotate("text", x = 6, y = 35, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 6, y = 33, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 6, y = 31, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Parity-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Parity, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Parity") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 6, y = 50, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 6, y = 47, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 6, y = 44, label = paste("n = ", length(DNAm.Age.Residual), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/Parity-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Age at first birth
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Age.FB",] >= 0 & !is.na(df.cov.K["Age.FB",])) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Age.FB",] >= 0 & !is.na(df.cov.K["Age.FB",])) ]))
Age.FB <- as.numeric(as.vector(df.cov.K["Age.FB", (df.cov.K["Age.FB",] >= 0 & !is.na(df.cov.K["Age.FB",])) ]))
DNAm.Age.Rho <- cor.test(Age.FB, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Age.FB, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Age.FB, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Age.FB, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Age.FB", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Age.FB", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Age.FB", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Age.FB", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Age.FB, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Age.FB, y=DNAm.Age)) +
geom_point() +
labs(x = "Age at First Birth") +
labs(y = "DNAm Age") +
annotate("text", x = 40, y = 80, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 40, y = 78, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 40, y = 76, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/AgeAtFirstBirth-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Age.FB, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Age at First Birth") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 40, y = 50, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 40, y = 47, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 40, y = 44, label = paste("n = ", length(DNAm.Age.Residual), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/AgeAtFirstBirth-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Menstural Status
##########################################################################
##### Plot Correlations #####
MenopausePost <- as.numeric(as.vector(df.cov.K["MenopausePost-menopausal", (df.cov.K["MenopausePost-menopausal",] == 1 & !is.na(df.cov.K["MenopausePost-menopausal",]))]))
MenopausePre <- as.numeric(as.vector(df.cov.K["MenopausePre-menopausal", (df.cov.K["MenopausePre-menopausal",] == 1 & !is.na(df.cov.K["MenopausePre-menopausal",]))]))
DNAm.Age.MenopausePost <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["MenopausePost-menopausal",] == 1 & !is.na(df.cov.K["MenopausePost-menopausal",]))]))
DNAm.Age.MenopausePre <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["MenopausePre-menopausal",] == 1 & !is.na(df.cov.K["MenopausePre-menopausal",]))]))
DNAm.Age.Residual.MenopausePost <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["MenopausePost-menopausal",] == 1 & !is.na(df.cov.K["MenopausePost-menopausal",]))]))
DNAm.Age.Residual.MenopausePre <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["MenopausePre-menopausal",] == 1 & !is.na(df.cov.K["MenopausePre-menopausal",]))]))
menopause <- c("0.Pre-menopausal", "1.Post-menopausal")
Mean.DNAm.Age <- as.numeric( c(mean(DNAm.Age.MenopausePre), mean(DNAm.Age.MenopausePost)) )
std.DNAm.Age <- as.numeric( c(sd(DNAm.Age.MenopausePre), sd(DNAm.Age.MenopausePost)) )
Mean.DNAm.Age.Residual <- as.numeric( c(mean(DNAm.Age.Residual.MenopausePre), mean(DNAm.Age.Residual.MenopausePost)) )
std.DNAm.Age.Residual <- as.numeric( c(sd(DNAm.Age.Residual.MenopausePre), sd(DNAm.Age.Residual.MenopausePost)) )
##### Correlate DNAm Age and Residual to Menopause #####
MenopausePre[MenopausePre == 1] <- 0
DNAm.Age <- as.numeric( c(DNAm.Age.MenopausePre, DNAm.Age.MenopausePost) )
DNAm.Age.Residual <- as.numeric( c(DNAm.Age.Residual.MenopausePre, DNAm.Age.Residual.MenopausePost) )
Menopause <- as.numeric( c(MenopausePre, MenopausePost) )
DNAm.Age.Rho <- cor.test(Menopause, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Menopause, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Menopause, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Menopause, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Menopause", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Menopause", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Menopause", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Menopause", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
df.tmp <- data.frame(menopause, Mean.DNAm.Age, std.DNAm.Age, Mean.DNAm.Age.Residual, std.DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=menopause, y=Mean.DNAm.Age)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age - std.DNAm.Age, ymax = Mean.DNAm.Age + std.DNAm.Age), width = 0.2) +
labs(x = "Menopause Status") +
labs(y = "DNAm Age") +
annotate("text", x = 0.75, y = 69, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = 66, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age[1]+2, label = paste("n = ", length(DNAm.Age.MenopausePre), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age[2]+2, label = paste("n = ", length(DNAm.Age.MenopausePost), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/MenopauseStatus-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = menopause, y = Mean.DNAm.Age.Residual)) +
geom_bar(stat="identity", width = 0.5) +
geom_errorbar(aes(ymin = Mean.DNAm.Age.Residual - std.DNAm.Age.Residual, ymax = Mean.DNAm.Age.Residual + std.DNAm.Age.Residual), width = 0.2) +
labs(x = "Menopause Status") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 0.75, y = -5, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +
annotate("text", x = 0.75, y = -6, label = paste("p value: ", round(DNAm.Age.Residual.pval, digits = 4), sep = "")) +
annotate("text", x = 1, y = Mean.DNAm.Age.Residual[1]+0.5, label = paste("n = ", length(DNAm.Age.Residual.MenopausePre), sep = ""), col = "blue") +
annotate("text", x = 2, y = Mean.DNAm.Age.Residual[2]+0.5, label = paste("n = ", length(DNAm.Age.Residual.MenopausePost), sep = ""), col = "blue") +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/MenopauseStatus-DNAmAgeResidual.png", sep = '') )
p
dev.off()
##########################################################################
# Univariate Analysis - Age at Menopause
##########################################################################
##### Correlate DNAm Age and Residual to Factor #####
DNAm.Age <- as.numeric(as.vector(df.cov.K["DNAm.Age", (df.cov.K["Menopause.Age",] >= 0 & !is.na(df.cov.K["Menopause.Age",])) ]))
DNAm.Age.Residual <- as.numeric(as.vector(df.cov.K["DNAm.Age.Residual", (df.cov.K["Menopause.Age",] >= 0 & !is.na(df.cov.K["Menopause.Age",])) ]))
Age.Menopause <- as.numeric(as.vector(df.cov.K["Menopause.Age", (df.cov.K["Menopause.Age",] >= 0 & !is.na(df.cov.K["Menopause.Age",])) ]))
DNAm.Age.Rho <- cor.test(Age.Menopause, DNAm.Age, method = "spearman" )$estimate
DNAm.Age.pval <- cor.test(Age.Menopause, DNAm.Age, method = "spearman" )$p.value
DNAm.Age.Residual.Rho <- cor.test(Age.Menopause, DNAm.Age.Residual, method = "spearman" )$estimate
DNAm.Age.Residual.pval <- cor.test(Age.Menopause, DNAm.Age.Residual, method = "spearman" )$p.value
df.univar["Age.Menopause", "DNAm.Age.Rho"] <- DNAm.Age.Rho
df.univar["Age.Menopause", "DNAm.Age.pval"] <- DNAm.Age.pval
df.univar["Age.Menopause", "DNAm.Age.Residual.Rho"] <- DNAm.Age.Residual.Rho
df.univar["Age.Menopause", "DNAm.Age.Residual.pval"] <- DNAm.Age.Residual.pval
##### Plot Correlations #####
df.tmp <- data.frame(Age.Menopause, DNAm.Age, DNAm.Age.Residual)
p <- ggplot(df.tmp, aes(x=Age.Menopause, y=DNAm.Age)) +
geom_point() +
labs(x = "Age at Menopause") +
labs(y = "DNAm Age") +
annotate("text", x = 28, y = 35, label = paste("Spearman Rho: ", round(DNAm.Age.Rho, digits = 3), sep = "")) +
annotate("text", x = 28, y = 33, label = paste("p value: ", round(DNAm.Age.pval, digits = 4), sep = "")) +
annotate("text", x = 28, y = 31, label = paste("n = ", length(DNAm.Age), sep = "")) +
theme_bw() +
theme(
axis.ticks.length=unit(-0.25, "cm"),
axis.text.x = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
axis.text.y = element_text(margin=unit(c(0.5,0.5,0.5,0.5), "cm")),
plot.title = element_text(hjust = 0.5)
)
png( paste(model.dir, "FactorAssociation/MenopauseAge-DNAmAge.png", sep = '') )
p
dev.off()
p <- ggplot(df.tmp, aes(x = Age.Menopause, y = DNAm.Age.Residual)) +
geom_point() +
labs(x = "Age at Menopause") +
labs(y = "DNAm Age Acceleration") +
annotate("text", x = 28, y = -22, label = paste("Spearman Rho: ", round(DNAm.Age.Residual.Rho, digits = 3), sep = "")) +