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13_CSF_GLM.r
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#################################################################################
#################################################################################
#################################################################################
# Carrier
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 1)
combined_data2 <- subset(combined_data2, DX == 0)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_carrier_DX_cat_CN_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}
#################################################################################
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 1)
combined_data2 <- subset(combined_data2, DX == 1)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_carrier_DX_cat_MCI_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}
#################################################################################
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 1)
combined_data2 <- subset(combined_data2, DX == 2)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_carrier_DX_cat_AD_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}
#################################################################################
#################################################################################
#################################################################################
# Non-Carrier
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 0)
combined_data2 <- subset(combined_data2, DX == 0)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_non_carrier_DX_cat_CN_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}
#################################################################################
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 0)
combined_data2 <- subset(combined_data2, DX == 1)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_non_carrier_DX_cat_MCI_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}
#################################################################################
# Limpa o ambiente
rm(list = ls())
# Carrega as bibliotecas necessárias
library(lme4)
library(lmerTest) # Fornece valores-p para modelos mistos
library(openxlsx)
library(broom.mixed)
library(dplyr)
library(ggplot2)
library(sandwich)
library(msm)
library(lmtest)
# Define o diretório de trabalho e carrega os dados
setwd('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/data/')
lipid_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Plasma')
phenotype_data <- read.xlsx('Filtered_Data.xlsx', sheet = 'Filtered_Pheno')
class_data <- read.xlsx('class_plasma.xlsx')
class_data$Variable <- class_data$Lipids
# Combinando as bases de dados
lipid_data$RID <- as.double(lipid_data$RID)
phenotype_data$RID <- as.double(phenotype_data$RID)
combined_data <- phenotype_data %>% left_join(lipid_data, by = "RID")
combined_data2 <- subset(combined_data, ApoE4_cat == 0)
combined_data2 <- subset(combined_data2, DX == 2)
ls(phenotype_data)
# Lista de desfechos a serem analisados-------------------------------------- |||||||
outcomes <- c("Abeta40_csf", "Abeta42_csf", "Abeta42_A40_csf", "PTAU", "TAU", "TAU_Abeta42")
#-------------------------------------- |||||||-------------------------------------- |||||||
for (outcome in outcomes) {
results_df <- data.frame(Variable = character(0),
Estimate = numeric(0),
SE = numeric(0),
P.value = numeric(0),
P.value.adjusted = numeric(0))
variables <- setdiff(colnames(lipid_data), "RID")
for (var in variables) {
formula_str <- as.formula(paste(var, "~", outcome, "+ Education + Age + SEX + BMI + statin"))
model <- glm(formula_str, family = "gaussian"(link = "identity"), data = combined_data2)
model_summary <- summary(model)
se <- sqrt(diag(vcovHC(model, type = "HC0")))
est <- coef(model)
temp_df <- data.frame(Variable = var,
Estimate = est[outcome],
SE = se[outcome])
temp_df$P.value <- 2 * pnorm(-abs(est[outcome]/se[outcome]))
results_df <- rbind(results_df, temp_df)
}
results_df$P.value.adjusted <- p.adjust(results_df$P.value, method = "BH")
results_df <- results_df[, c("Variable", "Estimate", "SE", "P.value", "P.value.adjusted")]
# Combina os resultados com a base de dados class_data baseada na coluna 'Variable'
results_df <- merge(results_df, class_data, by = "Variable", all.x = TRUE)
# Salva os resultados em um arquivo Excel, com o nome baseado no desfecho analisado
filename <- paste("GLM_ApoE4_non_carrier_DX_cat_AD_", outcome, "_adj.xlsx", sep = "")
write.xlsx(results_df, file.path('C:/Users/wjd4002/Documents/William/Project/ADNI/ADNI_wjd/Proj12/results/DX/', filename))
}