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cellProportions.Rmd
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Cell Proportions Graphs
Load Libraries
```{r}
library(ggplot2)
library(magrittr)
library(dplyr)
library(ggbeeswarm)
library(broom)
library(ggpubr)
library(patchwork)
library(data.table)
library(reshape2)
library(tidyr)
```
Data Filtering
```{r}
# Load files
patient_info <- '/Users/hugohudson/Desktop/Lab/GSE74193_GEO_procData.csv'
in_depth_patient_info <- '/Users/hugohudson/Desktop/Lab/GSE74193_series_matrix.csv'
col_names <- names(fread(patient_info, nrows = 0))
beta_columns <- col_names[grepl("Beta", col_names)]
methylation_df <- fread(patient_info, select = beta_columns)
col_names <- colnames(methylation_df)
patient_df <- read.csv(in_depth_patient_info)
# Create a dataframe of methylation data that is aligned with the cell fractions, i.e. exclude if we do not have cell fractions
brnum <- substr(patient_df[43, 2:ncol(patient_df)], 50, nchar(patient_df[43, 2]))
colnames(methylation_df) <- brnum
colnames(methylation_df) <- make.names(colnames(methylation_df), unique = TRUE)
uniqueDF <- select(methylation_df, colnames(methylation_df)[!grepl("\\.1$", colnames(methylation_df))])
# Now select the ones in unique that have DAN's brnums
danCSV <- read.csv('/Users/hugohudson/Desktop/Lab/epigenetic_age_added.csv')
filteredDAN <- filter(danCSV, grepl('Br', danCSV$individualID))
accurateDF <- select(uniqueDF, colnames(uniqueDF)[colnames(uniqueDF) %in% filteredDAN$individualID])
danFILTER <- filteredDAN %>%
filter(individualID %in% colnames(accurateDF))
# Make df with sorted brnum and fractions
df <- transpose(danFILTER[, 9:ncol(danFILTER)])
nextNames <- df[1, ]
df <- df[2:nrow(df), ]
colnames(df) <- nextNames
# return the above df and sorted methylation
patientData <- df[sort(colnames(df))]
methylationData <- select(accurateDF, sort(colnames(accurateDF)))
cell_types <- c("Astrocyte", "Endothelial", "IT", "L4.IT", "L5.ET",
"L5.6.IT.Car3", "L5.6.NP", "L6.CT", "L6b", "LAMP5",
"Microglia", "OPC", "Oligodendrocyte", "PAX6",
"PVALB", "Pericyte", "SST", "VIP", "VLMC")
cellProp <- t(patientData[28:nrow(patientData), ])
colnames(cellProp) <- c(cell_types, 'epigeneticAge')
cellProp <- as.data.frame(cellProp)
previousNames <- colnames(cellProp)
cellProp <- cbind(cellProp, t(patientData[17, ]), t(patientData[18, ]), t(patientData[19, ]), t(patientData[20, ]))
colnames(cellProp) <- c(previousNames, 'diagnosis', 'gender', 'race', 'age')
epiLM <-lm(as.numeric(age) ~ as.numeric(epigeneticAge), data = cellProp)
model_residuals <- residuals(epiLM)
epigeneticAge <- cellProp$epigeneticAge
epigeneticAge<-as.numeric(cellProp$epigeneticAge)
predicted_ages <- predict(epiLM, newdata = as.data.frame(epigeneticAge))
cellProp$epigeneticAge <- predicted_ages
```
Graph Cell Proportions
```{r}
astrocyteModel <- lm(as.numeric(Astrocyte) ~ epigeneticAge * diagnosis, data = cellProp)
astrocyteSummary <- summary(astrocyteModel)
astrocytepValInteract <- astrocyteSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (astrocytepValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(Astrocyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Astrocyte Proportions",
x = "Epigenetic Age",
y = "Astrocyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
EndothelialModel <- lm(as.numeric(Endothelial) ~ epigeneticAge * diagnosis, data = cellProp)
EndothelialSummary <- summary(EndothelialModel)
EndothelialpValInteract <- EndothelialSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (EndothelialpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(Endothelial))) +
geom_point() +
labs( title = "Epigenetic Age vs Endothelial Proportions",
x = "Epigenetic Age",
y = "Endothelial Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
ITModel <- lm(as.numeric(IT) ~ epigeneticAge * diagnosis, data = cellProp)
ITSummary <- summary(ITModel)
ITpValInteract <- ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(IT))) +
geom_point() +
labs( title = "Epigenetic Age vs IT Proportions",
x = "Epigenetic Age",
y = "IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L4.IT) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L4.IT))) +
geom_point() +
labs( title = "Epigenetic Age vs L4.IT Proportions",
x = "Epigenetic Age",
y = "L4.IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.ETModel <- lm(as.numeric(L5.ET) ~ epigeneticAge * diagnosis, data = cellProp)
L5.ETSummary <- summary(L5.ETModel)
L5.ETpValInteract <- L5.ETSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L5.ETpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L5.ET))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.ET Proportions",
x = "Epigenetic Age",
y = "L5.ET Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.IT.Car3Model <- lm(as.numeric(L5.6.IT.Car3) ~ epigeneticAge * diagnosis, data = cellProp)
L5.6.IT.Car3Summary <- summary(L5.6.IT.Car3Model)
L5.6.IT.Car3pValInteract <- L5.6.IT.Car3Summary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L5.6.IT.Car3pValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L5.6.IT.Car3))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.IT.Car3 Proportions",
x = "Epigenetic Age",
y = "L5.6.IT.Car3 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.NPModel <- lm(as.numeric(L5.6.NP) ~ epigeneticAge * diagnosis, data = cellProp)
L5.6.NPSummary <- summary(L5.6.NPModel)
L5.6.NPpValInteract <- L5.6.NPSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L5.6.NPpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L5.6.NP))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.NP Proportions",
x = "Epigenetic Age",
y = "L5.6.NP Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L6.CTModel <- lm(as.numeric(L6.CT) ~ epigeneticAge * diagnosis, data = cellProp)
L6.CTSummary <- summary(L6.CTModel)
L6.CTpValInteract <- L6.CTSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L6.CTpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L6.CT))) +
geom_point() +
labs( title = "Epigenetic Age vs L6.CT Proportions",
x = "Epigenetic Age",
y = "L6.CT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L6b) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(L6b))) +
geom_point() +
labs( title = "Epigenetic Age vs L6b Proportions",
x = "Epigenetic Age",
y = "L6b Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(LAMP5) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(LAMP5))) +
geom_point() +
labs( title = "Epigenetic Age vs LAMP5 Proportions",
x = "Epigenetic Age",
y = "LAMP5 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Microglia) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(Microglia))) +
geom_point() +
labs( title = "Epigenetic Age vs Microglia Proportions",
x = "Epigenetic Age",
y = "Microglia Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(OPC) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(OPC))) +
geom_point() +
labs( title = "Epigenetic Age vs OPC Proportions",
x = "Epigenetic Age",
y = "OPC Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Oligodendrocyte) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(Oligodendrocyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Oligodendrocyte Proportions",
x = "Epigenetic Age",
y = "Oligodendrocyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(PAX6) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(PAX6))) +
geom_point() +
labs( title = "Epigenetic Age vs PAX6 Proportions",
x = "Epigenetic Age",
y = "PAX6 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(PVALB) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(PVALB))) +
geom_point() +
labs( title = "Epigenetic Age vs PVALB Proportions",
x = "Epigenetic Age",
y = "PVALB Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Pericyte) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(Pericyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Pericyte Proportions",
x = "Epigenetic Age",
y = "Pericyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(SST) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(SST))) +
geom_point() +
labs( title = "Epigenetic Age vs SST Proportions",
x = "Epigenetic Age",
y = "SST Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(VIP) ~ epigeneticAge * diagnosis, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(VIP))) +
geom_point() +
labs( title = "Epigenetic Age vs VIP Proportions",
x = "Epigenetic Age",
y = "VIP Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
vlmcModel <- lm(as.numeric(VLMC) ~ epigeneticAge * diagnosis, data = cellProp)
vlmcSummary <- summary(vlmcModel)
vlmcpValInteract <- vlmcSummary$coefficients['epigeneticAge:diagnosisSchizophrenia', 'Pr(>|t|)']
if (vlmcpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(vlmcpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(vlmcpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = diagnosis, x = epigeneticAge, y = as.numeric(VLMC))) +
geom_point() +
labs( title = "Epigenetic Age vs VLMC Proportions",
x = "Epigenetic Age",
y = "VLMC Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
cellProp$epi_age_groups <- cut(x = cellProp$epigeneticAge, breaks = seq(10, 100, by = 10), labels = c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+"))
ggplot(data = cellProp, mapping = aes(x = epi_age_groups, y = model_residuals, color = diagnosis)) +
geom_boxplot() +
labs( x = "Chronological Age Groups", y = "Residuals", title = "Residuals for Chronological Age Groups")
data_long <- cellProp %>%
pivot_longer(
cols = Astrocyte:VLMC,
names_to = 'CellType',
values_to = 'Proportion'
)
data_long
data_long$residual <- as.numeric(data_long$epigeneticAge) - as.numeric(data_long$age)
p <- ggplot(data_long, mapping = aes(residual, as.numeric(Proportion), colour = diagnosis)) + geom_point() + ylab('Cell Proportion')
p + facet_wrap(vars(CellType))
head(cellProp)
lm(as.numeric(cellProp$Astrocyte) ~ as.numeric(cellProp$age))
cellProp$Astrocyte
model <- lapply(split(data_long, data_long$CellType), function(subdata) {
lm(Proportion ~ age, data = subdata)
})
model2 <- lapply(split(data_long, data_long$CellType), function(subdata) {
lm(Proportion ~ age + epigeneticAge, data = subdata)
})
model2
model
anova_results <- lapply(cell_types, function(var) {
# Extract the models for the current response variable
model1 <- model[[var]]
model22 <- model2[[var]]
# Perform ANOVA
result <- anova(model1, model22)
return(data.frame(variable = var, result))
})
anova_summary <- do.call(rbind, anova_results)
filtered_summary <- anova_summary[!is.na(anova_summary$Df), ]
filtered_summary[filtered_summary$Pr..F. < 0.005, ]
filtered_summary
# remove outlier
data_long <- data_long[data_long$residual > -18, ]
model <- lapply(split(data_long, data_long$CellType), function(subdata) {
lm(Proportion ~ age, data = subdata)
})
model2 <- lapply(split(data_long, data_long$CellType), function(subdata) {
lm(Proportion ~ age + epigeneticAge, data = subdata)
})
anova_results <- lapply(cell_types, function(var) {
# Extract the models for the current response variable
model1 <- model[[var]]
model22 <- model2[[var]]
# Perform ANOVA
result <- anova(model1, model22)
return(data.frame(variable = var, result))
})
anova_summary <- do.call(rbind, anova_results)
p <- ggplot(data_long, mapping = aes(residual, as.numeric(Proportion), colour = diagnosis)) + geom_point() + ylab('Cell Proportion') +
stat_smooth(method = "lm") +
stat_cor()
p + facet_wrap(vars(CellType))
```
```{r}
# Load necessary libraries
library(dplyr)
# Initialize a data frame to store results
results <- data.frame(AgeBin = character(),
Test = character(),
p.value = numeric(),
stringsAsFactors = FALSE)
# Loop through each age bin
for (age_bin in unique(cellProp$epi_age_groups)) {
# Subset the data for the current age bin
subset_data <- cellProp[cellProp$epi_age_groups == age_bin, ]
# Split values by group
group1_values <- as.numeric(subset_data$epigeneticAge[subset_data$diagnosis == "control"])
group2_values <- as.numeric(subset_data$epigeneticAge[subset_data$diagnosis == "Schizophrenia"])
# Test for normality
if (length(group1_values) > 3 & length(group2_values) > 3){
shapiro_test_group1 <- shapiro.test(group1_values)
shapiro_test_group2 <- shapiro.test(group2_values)
# Choose the appropriate test based on normality
if (shapiro_test_group1$p.value > 0.05 && shapiro_test_group2$p.value > 0.05) {
# Both groups are normally distributed
t_test_result <- t.test(group1_values, group2_values)
test_type <- "t-test"
p_value <- t_test_result$p.value
} else {
# At least one group is not normally distributed
mann_whitney_result <- wilcox.test(group1_values, group2_values)
test_type <- "Mann-Whitney U test"
p_value <- mann_whitney_result$p.value
}
# Append the results to the results data frame
results <- rbind(results, data.frame(AgeBin = age_bin,
Test = test_type,
p.value = p_value,
stringsAsFactors = FALSE))
}
else{
results <- rbind(results, data.frame(AgeBin = age_bin,
Test = 'NULL',
p.value = 'NULL',
stringsAsFactors = FALSE))
}
}
# View the results
print(results)
```
For Ancestry / Race
```{r}
astrocyteModel <- lm(as.numeric(Astrocyte) ~ epigeneticAge * race, data = cellProp)
astrocyteSummary <- summary(astrocyteModel)
astrocytepValInteract <- astrocyteSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (astrocytepValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(Astrocyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Astrocyte Proportions",
x = "Epigenetic Age",
y = "Astrocyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
EndothelialModel <- lm(as.numeric(Endothelial) ~ epigeneticAge * race, data = cellProp)
EndothelialSummary <- summary(EndothelialModel)
EndothelialpValInteract <- EndothelialSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (EndothelialpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(Endothelial))) +
geom_point() +
labs( title = "Epigenetic Age vs Endothelial Proportions",
x = "Epigenetic Age",
y = "Endothelial Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
ITModel <- lm(as.numeric(IT) ~ epigeneticAge * race, data = cellProp)
ITSummary <- summary(ITModel)
ITpValInteract <- ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(IT))) +
geom_point() +
labs( title = "Epigenetic Age vs IT Proportions",
x = "Epigenetic Age",
y = "IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L4.IT) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L4.IT))) +
geom_point() +
labs( title = "Epigenetic Age vs L4.IT Proportions",
x = "Epigenetic Age",
y = "L4.IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.ETModel <- lm(as.numeric(L5.ET) ~ epigeneticAge * race, data = cellProp)
L5.ETSummary <- summary(L5.ETModel)
L5.ETpValInteract <- L5.ETSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L5.ETpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L5.ET))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.ET Proportions",
x = "Epigenetic Age",
y = "L5.ET Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.IT.Car3Model <- lm(as.numeric(L5.6.IT.Car3) ~ epigeneticAge * race, data = cellProp)
L5.6.IT.Car3Summary <- summary(L5.6.IT.Car3Model)
L5.6.IT.Car3pValInteract <- L5.6.IT.Car3Summary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L5.6.IT.Car3pValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L5.6.IT.Car3))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.IT.Car3 Proportions",
x = "Epigenetic Age",
y = "L5.6.IT.Car3 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.NPModel <- lm(as.numeric(L5.6.NP) ~ epigeneticAge * race, data = cellProp)
L5.6.NPSummary <- summary(L5.6.NPModel)
L5.6.NPpValInteract <- L5.6.NPSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L5.6.NPpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L5.6.NP))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.NP Proportions",
x = "Epigenetic Age",
y = "L5.6.NP Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L6.CTModel <- lm(as.numeric(L6.CT) ~ epigeneticAge * race, data = cellProp)
L6.CTSummary <- summary(L6.CTModel)
L6.CTpValInteract <- L6.CTSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L6.CTpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L6.CT))) +
geom_point() +
labs( title = "Epigenetic Age vs L6.CT Proportions",
x = "Epigenetic Age",
y = "L6.CT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L6b) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(L6b))) +
geom_point() +
labs( title = "Epigenetic Age vs L6b Proportions",
x = "Epigenetic Age",
y = "L6b Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(LAMP5) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(LAMP5))) +
geom_point() +
labs( title = "Epigenetic Age vs LAMP5 Proportions",
x = "Epigenetic Age",
y = "LAMP5 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Microglia) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(Microglia))) +
geom_point() +
labs( title = "Epigenetic Age vs Microglia Proportions",
x = "Epigenetic Age",
y = "Microglia Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(OPC) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(OPC))) +
geom_point() +
labs( title = "Epigenetic Age vs OPC Proportions",
x = "Epigenetic Age",
y = "OPC Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Oligodendrocyte) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(Oligodendrocyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Oligodendrocyte Proportions",
x = "Epigenetic Age",
y = "Oligodendrocyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(PAX6) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(PAX6))) +
geom_point() +
labs( title = "Epigenetic Age vs PAX6 Proportions",
x = "Epigenetic Age",
y = "PAX6 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(PVALB) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(PVALB))) +
geom_point() +
labs( title = "Epigenetic Age vs PVALB Proportions",
x = "Epigenetic Age",
y = "PVALB Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(Pericyte) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(Pericyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Pericyte Proportions",
x = "Epigenetic Age",
y = "Pericyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(SST) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(SST))) +
geom_point() +
labs( title = "Epigenetic Age vs SST Proportions",
x = "Epigenetic Age",
y = "SST Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(VIP) ~ epigeneticAge * race, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(VIP))) +
geom_point() +
labs( title = "Epigenetic Age vs VIP Proportions",
x = "Epigenetic Age",
y = "VIP Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
vlmcModel <- lm(as.numeric(VLMC) ~ epigeneticAge * race, data = cellProp)
vlmcSummary <- summary(vlmcModel)
vlmcpValInteract <- vlmcSummary$coefficients['epigeneticAge:raceCAUC', 'Pr(>|t|)']
if (vlmcpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(vlmcpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(vlmcpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = race, x = epigeneticAge, y = as.numeric(VLMC))) +
geom_point() +
labs( title = "Epigenetic Age vs VLMC Proportions",
x = "Epigenetic Age",
y = "VLMC Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
cellProp$epi_age_groups <- cut(x = cellProp$epigeneticAge, breaks = seq(10, 100, by = 10), labels = c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+"))
ggplot(data = cellProp, mapping = aes(x = epi_age_groups, y = model_residuals, color = race)) +
geom_boxplot() +
labs( x = "Chronological Age Groups", y = "Residuals", title = "Residuals for Chronological Age Groups")
```
For Gender
```{r}
astrocyteModel <- lm(as.numeric(Astrocyte) ~ epigeneticAge * gender, data = cellProp)
astrocyteSummary <- summary(astrocyteModel)
astrocytepValInteract <- astrocyteSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (astrocytepValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(astrocytepValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(Astrocyte))) +
geom_point() +
labs( title = "Epigenetic Age vs Astrocyte Proportions",
x = "Epigenetic Age",
y = "Astrocyte Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
EndothelialModel <- lm(as.numeric(Endothelial) ~ epigeneticAge * gender, data = cellProp)
EndothelialSummary <- summary(EndothelialModel)
EndothelialpValInteract <- EndothelialSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (EndothelialpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(EndothelialpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(Endothelial))) +
geom_point() +
labs( title = "Epigenetic Age vs Endothelial Proportions",
x = "Epigenetic Age",
y = "Endothelial Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
ITModel <- lm(as.numeric(IT) ~ epigeneticAge * gender, data = cellProp)
ITSummary <- summary(ITModel)
ITpValInteract <- ITSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(IT))) +
geom_point() +
labs( title = "Epigenetic Age vs IT Proportions",
x = "Epigenetic Age",
y = "IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L4.IT) ~ epigeneticAge * gender, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L4.IT))) +
geom_point() +
labs( title = "Epigenetic Age vs L4.IT Proportions",
x = "Epigenetic Age",
y = "L4.IT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.ETModel <- lm(as.numeric(L5.ET) ~ epigeneticAge * gender, data = cellProp)
L5.ETSummary <- summary(L5.ETModel)
L5.ETpValInteract <- L5.ETSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L5.ETpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.ETpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L5.ET))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.ET Proportions",
x = "Epigenetic Age",
y = "L5.ET Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.IT.Car3Model <- lm(as.numeric(L5.6.IT.Car3) ~ epigeneticAge * gender, data = cellProp)
L5.6.IT.Car3Summary <- summary(L5.6.IT.Car3Model)
L5.6.IT.Car3pValInteract <- L5.6.IT.Car3Summary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L5.6.IT.Car3pValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.IT.Car3pValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L5.6.IT.Car3))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.IT.Car3 Proportions",
x = "Epigenetic Age",
y = "L5.6.IT.Car3 Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L5.6.NPModel <- lm(as.numeric(L5.6.NP) ~ epigeneticAge * gender, data = cellProp)
L5.6.NPSummary <- summary(L5.6.NPModel)
L5.6.NPpValInteract <- L5.6.NPSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L5.6.NPpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L5.6.NPpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L5.6.NP))) +
geom_point() +
labs( title = "Epigenetic Age vs L5.6.NP Proportions",
x = "Epigenetic Age",
y = "L5.6.NP Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L6.CTModel <- lm(as.numeric(L6.CT) ~ epigeneticAge * gender, data = cellProp)
L6.CTSummary <- summary(L6.CTModel)
L6.CTpValInteract <- L6.CTSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L6.CTpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L6.CTpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L6.CT))) +
geom_point() +
labs( title = "Epigenetic Age vs L6.CT Proportions",
x = "Epigenetic Age",
y = "L6.CT Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)
L4.ITModel <- lm(as.numeric(L6b) ~ epigeneticAge * gender, data = cellProp)
L4.ITSummary <- summary(L4.ITModel)
L4.ITpValInteract <- L4.ITSummary$coefficients['epigeneticAge:gendermale', 'Pr(>|t|)']
if (L4.ITpValInteract < 0.005) {
significance <- paste("Signficant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
} else {
significance <- paste("No significant difference in slopes (p=", round(L4.ITpValInteract, 3), ")")
}
ggplot(data = cellProp, mapping = aes(color = gender, x = epigeneticAge, y = as.numeric(L6b))) +
geom_point() +
labs( title = "Epigenetic Age vs L6b Proportions",
x = "Epigenetic Age",
y = "L6b Proportion") +
stat_smooth(method = "lm") +
stat_cor() +
annotate("text", x = 0, y = 7, label = significance, size = 4, color = "black", hjust = 0)