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5-2.glm_adult_unharmo.R
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##################################################################
## ENIGMA-OCD adult GLM ##
##################################################################
##########################################################################
########## 1. load data ##########
##########################################################################
###### load prediction probability data
setwd("~/Library/Mobile Documents/com~apple~CloudDocs/connectome21/2021-1/project/ENIGMA-OCD/final/4.h2oDAI/1.adult/1.Basic/T.Dx_D.nonharmo scaled only adult_cv.LOSO_777_F.age.sex_21.11.23")
train_pred <- read.csv("train_preds_custom.csv")
test_pred <- read.csv("test_preds_custom.csv")
##########################################################################
########## 2. data preprocessing ##########
##########################################################################
##### preprocessing for the analysis - factorization
train_varName_fact <- c(which(names(train_pred) == 'Dx'), which(names(train_pred) == 'Site'), which(names(train_pred) == 'Sex'),
which(names(train_pred) == 'Med'), which(names(train_pred) == 'Agr_Check'), which(names(train_pred) == 'Clean'),
which(names(train_pred) == 'Sex_Rel'),which(names(train_pred) == 'Hoard'), which(names(train_pred) == 'Ord'),
which(names(train_pred) == 'Anx'), which(names(train_pred) == 'CurAnx'), which(names(train_pred) == 'Dep'),
which(names(train_pred) == 'CurDep'), which(names(train_pred) == 'site_int'))
train_pred[,train_varName_fact] = lapply(train_pred[,train_varName_fact], factor)
test_varName_fact <- c(which(names(test_pred) == 'Dx'), which(names(test_pred) == 'Site'), which(names(test_pred) == 'Sex'),
which(names(test_pred) == 'Med'), which(names(test_pred) == 'Agr_Check'), which(names(test_pred) == 'Clean'),
which(names(test_pred) == 'Sex_Rel'), which(names(test_pred) == 'Hoard'), which(names(test_pred) == 'Ord'),
which(names(test_pred) == 'Anx'), which(names(test_pred) == 'CurAnx'), which(names(test_pred) == 'Dep'),
which(names(test_pred) == 'CurDep'), which(names(test_pred) == 'site_int'))
test_pred[,test_varName_fact] = lapply(test_pred[,test_varName_fact], factor)
##### missing values / re-coding
library(dplyr)
# 999
train_pred[train_pred == 999] <- NA
test_pred[test_pred == 999] <- NA
##### filtering by Dx group
train_pred_HC <- train_pred %>% filter(Dx == 0)
train_pred_OCD <- train_pred %>% filter(Dx == 1)
test_pred_HC <- test_pred %>% filter(Dx == 0)
test_pred_OCD <- test_pred %>% filter(Dx == 1)
##### missing value processing based on ENIGMA-OCD clinical criteria
# [HC] train_pred_HC, test_pred_HC
# MED=0 / AO - / Dur - / Sev - / Agr_Check - / Clean - / Sex_Rel - / Hoard - / Ord - / Anx 0 / CurAnx 0 / Dep 0 / CurDep 0
# -
# [OCD] train_pred_OCD, test_pred_OCD
train_pred_OCD$Med[train_pred_OCD$Med == 0] <- NA
train_pred_OCD$Anx[train_pred_OCD$Anx == 0] <- NA
train_pred_OCD$Dep[train_pred_OCD$Dep == 0] <- NA
train_pred_OCD$CurAnx[train_pred_OCD$CurAnx == 0] <- NA
train_pred_OCD$CurDep[train_pred_OCD$CurDep == 0] <- NA
train_pred_OCD$Agr_Check[train_pred_OCD$Agr_Check == 999] <- NA
train_pred_OCD$Clean[train_pred_OCD$Clean == 999] <- NA
train_pred_OCD$Ord[train_pred_OCD$Ord == 999] <- NA
train_pred_OCD$Sex_Rel[train_pred_OCD$Sex_Rel == 999] <- NA
train_pred_OCD$Hoard[train_pred_OCD$Hoard == 999] <- NA
test_pred_OCD$Med[test_pred_OCD$Med == 0] <- NA
test_pred_OCD$Anx[test_pred_OCD$Anx == 0] <- NA
test_pred_OCD$Dep[test_pred_OCD$Dep == 0] <- NA
test_pred_OCD$CurAnx[test_pred_OCD$CurAnx == 0] <- NA
test_pred_OCD$CurDep[test_pred_OCD$CurDep == 0] <- NA
test_pred_OCD$Agr_Check[test_pred_OCD$Agr_Check == 999] <- NA
test_pred_OCD$Clean[test_pred_OCD$Clean == 999] <- NA
test_pred_OCD$Ord[test_pred_OCD$Ord == 999] <- NA
test_pred_OCD$Sex_Rel[test_pred_OCD$Sex_Rel == 999] <- NA
test_pred_OCD$Hoard[test_pred_OCD$Hoard == 999] <- NA
train_pred_OCD <- droplevels(train_pred_OCD)
test_pred_OCD <- droplevels(test_pred_OCD)
###### normality test before the analysis
# normality test (O -> t-test ok)
# in train set
shapiro.test(train_pred$Dx.1) # W = 0.90032, p-value < 2.2e-16 -> high p-value means normality O
qqnorm(train_pred$Dx.1)
qqline(train_pred$Dx.1)
shapiro.test(train_pred_HC$Dx.1) # W = 0.90653, p-value < 2.2e-16
qqnorm(train_pred_HC$Dx.1)
qqline(train_pred_HC$Dx.1)
shapiro.test(train_pred_OCD$Dx.1) # W = 0.88295, p-value < 2.2e-16
qqnorm(train_pred_OCD$Dx.1)
qqline(train_pred_OCD$Dx.1)
# in test set
shapiro.test(test_pred$Dx.1) # W = 0.98963, p-value = 0.05297
qqnorm(test_pred$Dx.1)
qqline(train_pred$Dx.1)
shapiro.test(test_pred_HC$Dx.1) # W = 0.98211, p-value = 0.08462
qqnorm(test_pred_HC$Dx.1)
qqline(test_pred_HC$Dx.1)
shapiro.test(test_pred_OCD$Dx.1) # W = 0.99292, p-value = 0.7255
qqnorm(test_pred_OCD$Dx.1)
qqline(test_pred_OCD$Dx.1)
##########################################################################
########## 3. sample characteristics ##########
##########################################################################
##### description analysis of "Dx.1" (prediction probability) by "dx (diagnosis)" group
train_pred %>%
group_by(Dx) %>%
summarise(mean_value = mean(Dx.1, na.rm = T), sd_value = sd(Dx.1, na.rm = T))
test_pred %>%
group_by(Dx) %>%
summarise(mean_value = mean(Dx.1, na.rm = T), sd_value = sd(Dx.1, na.rm = T))
##########################################################################
########## 4. association between clinical variables and Preds ##########
##########################################################################
# Dx.1 ~ Med + Sex + Age + Site + AverageFA + AverageMD + AverageRD + AverageAD
### demo (age + sex + ...)
agesex_1 <- glm(Dx.1 ~ Age + Sex + Dx + Site, data = train_pred)
summary(agesex_1)
aov <- aov(agesex_1)
summary(aov)
### clinical association
## set (1) predictors & (2) covariates
#################### train set ####################
predictors_i_1 <- colnames(train_pred_OCD)[8:20] # (1) predictors # ****
#[predictors_i_1] Med, AO, Dur, Sev, Agr_Check, Clean, Sex_Rel, Hoard, Ord, Anx, CurAnx, Dep, CurDep
covars = c("Sex", "Age", "Site", "AverageFA", "AverageMD", "AverageRD", "AverageAD") # (2) covariates
## make formula
forms = paste('Dx.1 ~', predictors_i_1) # ****
for(i in 1:length(covars)){
forms = paste(forms, '+', covars[i])
}
## empty vector for coeff, p-value
temp.names <- vector()
name_lm <- vector()
temp.names <- vector()
name_aov <- vector()
#glm
temp.coeff <- vector()
temp.p <- vector()
#aov
temp.aov <- vector()
temp.DF <- vector()
temp.MeanSq <- vector()
temp.F <- vector()
temp.Pr <- vector()
## loop for glm w/ multiple variables
for(i in 1:length(forms)){
form = as.formula(forms[i])
# glm
temp.lm <- lm(formula = form, data = train_pred_OCD) # **** train_pred_OCD ***
name_lm[i] <- predictors_i_1[i]
temp.coeff[i] <- round(temp.lm[[1]][2], 3)
temp.p[i] <- round(coef(summary(temp.lm))[2,4], 3)
fdr <- round(p.adjust(temp.p, method = 'fdr'), 3)
glm_coeff_train <- data.frame(y = name_lm, beta = temp.coeff, p = temp.p, fdr)
# aov
temp.aov <- anova(temp.lm) #ancova
name_aov[i] <- predictors_i_1[i]
#temp.DF[i] <- round(temp.aov[1,1],5)
#temp.MeanSq[i] <- round(temp.aov[[3]][1],5)
temp.F[i] <- round(temp.aov[1,4],3)
temp.Pr[i] <- round(temp.aov[1,5],3)
fdr_aov <- round(p.adjust(temp.Pr, method = 'fdr'), 3)
aov_coeff_train <- data.frame(y = name_aov, f.value = temp.F, Pr = temp.Pr, fdr_aov)
}
View(glm_coeff_train)
View(aov_coeff_train)
#################### test set ####################
predictors_i_2 <- colnames(test_pred_OCD)[c(8:20)] # (1) predictors # ****
#[predictors_i_2] Med, AO, Dur, Sev, Agr_Check, Clean, Sex_Rel, Hoard, Ord, Anx, CurAnx, Dep
#CurDep level - only 1
covars = c("Sex", "Age", "Site", "AverageFA", "AverageMD", "AverageRD", "AverageAD") # (2) covariates
print(covars)
## make formula
forms = paste('Dx.1 ~', predictors_i_2) # ****
for(i in 1:length(covars)){
forms = paste(forms, '+', covars[i])
}
## empty vector for coeff, p-value
temp.names <- vector()
name_lm <- vector()
temp.names <- vector()
name_aov <- vector()
#glm
temp.coeff <- vector()
temp.p <- vector()
#aov
temp.aov <- vector()
temp.DF <- vector()
temp.MeanSq <- vector()
temp.F <- vector()
temp.Pr <- vector()
## loop for glm w/ multiple variables
for(i in 1:length(forms)){
form = as.formula(forms[i])
# glm
temp.lm <- lm(formula = form, data = test_pred_OCD) # **** train_pred_OCD ***
name_lm[i] <- predictors_i_2[i]
temp.coeff[i] <- round(temp.lm[[1]][2], 3)
temp.p[i] <- round(coef(summary(temp.lm))[2,4], 3)
fdr <- p.adjust(temp.p, method = 'fdr')
glm_coeff_test <- data.frame(y = name_lm, beta = temp.coeff, p = temp.p, fdr)
# aov
temp.aov <- anova(temp.lm) #ancova
name_aov[i] <- predictors_i_1[i]
#temp.DF[i] <- round(temp.aov[1,1],5)
#temp.MeanSq[i] <- round(temp.aov[[3]][1],5)
temp.F[i] <- round(temp.aov[1,4],3)
temp.Pr[i] <- round(temp.aov[1,5],3)
fdr_aov <- round(p.adjust(temp.Pr, method = 'fdr'), 3)
aov_coeff_test <- data.frame(y = name_aov, f.value = temp.F, Pr = temp.Pr, fdr_aov)
}
View(glm_coeff_test)
View(aov_coeff_test)
##########################################################################
########## 5. averageFA/MD/AD/RD across site (site variability) ##########
##########################################################################
#################### train set ####################
siteFA <- glm(AverageFA ~ Site + Sex + Age, data = train_pred_OCD) #train_pred_HC / train_pred_OCD
siteMD <- glm(AverageMD ~ Site + Sex + Age, data = train_pred_OCD) #train_pred_HC / train_pred_OCD
siteAD <- glm(AverageAD ~ Site + Sex + Age, data = train_pred_OCD) #train_pred_HC / train_pred_OCD
siteRD <- glm(AverageRD ~ Site + Sex + Age, data = train_pred_OCD) #train_pred_HC / train_pred_OCD
aov_siteFA <- aov(siteFA)
aov_siteMD <- aov(siteMD)
aov_siteAD <- aov(siteAD)
aov_siteRD <- aov(siteRD)
summary(aov_siteFA)
summary(aov_siteMD)
summary(aov_siteAD)
summary(aov_siteRD)
#################### test set ####################
siteFA <- glm(AverageFA ~ Site + Sex + Age, data = test_pred_OCD) #test_pred_HC / test_pred_OCD
siteMD <- glm(AverageMD ~ Site + Sex + Age, data = test_pred_OCD) #test_pred_HC / test_pred_OCD
siteAD <- glm(AverageAD ~ Site + Sex + Age, data = test_pred_OCD) #test_pred_HC / test_pred_OCD
siteRD <- glm(AverageRD ~ Site + Sex + Age, data = test_pred_OCD) #test_pred_HC / test_pred_OCD
aov_siteFA <- aov(siteFA)
aov_siteMD <- aov(siteMD)
aov_siteAD <- aov(siteAD)
aov_siteRD <- aov(siteRD)
summary(aov_siteFA)
summary(aov_siteMD)
summary(aov_siteAD)
summary(aov_siteRD)
##########################################################################
########## 6-1. [train] individual performance across site (site variability) ##########
##########################################################################
#################### mutate threshold ####################
library(dplyr)
train_pred <- train_pred %>%
mutate(threshold = ifelse(Site == 'Amsterdam', 0.5606682,
ifelse(Site == 'Bangalore', 0.5467443,
ifelse(Site == 'Capetown', 0.523335,
ifelse(Site == 'Kyoto', 0.5231088,
ifelse(Site == 'Milan', 0.5285721,
ifelse(Site == 'Mountsinal', 0.5327545,
ifelse(Site == 'Munich', 0.5243237,
ifelse(Site == 'Rome', 0.4470824,
ifelse(Site == 'Saopaulo', 0.5288856,
ifelse(Site == 'Seoul', 0.5531548,
ifelse(Site == 'Shanghai', 0.4738248, NA))))))))))))
#################### check data ####################
str(train_pred$threshold)
hist(train_pred$threshold)
sum(is.na(train_pred$threshold)) # 30
train_pred %>%
xtabs(~ Site, addNA = TRUE, data = .)
#################### create confusion matrix variable ####################
train_pred <- train_pred %>%
mutate(Dx_predicted = ifelse(Dx.1 >= threshold, 1, 0))
train_pred <- train_pred %>%
mutate(ConfusionMatrix = ifelse(Dx_predicted == 0 & Dx == 0, 'TN',
ifelse(Dx_predicted ==1 & Dx == 1, 'TP',
ifelse(Dx_predicted == 0 & Dx == 1, 'FN',
ifelse(Dx_predicted == 1 & Dx == 0, 'FP', NA)))))
#################### check TRUE / FALSE ####################
# True prediction [proba_val] - TN, TP = 1, FN, FP = 0 / False_prediction_total - (reverse)
train_pred <- train_pred %>%
mutate(True_prediction_total = ifelse(ConfusionMatrix == 'TN' | ConfusionMatrix == 'TP', 1, 0),
False_prediction_total = ifelse(ConfusionMatrix == 'FN' | ConfusionMatrix == 'FP', 1, 0))
train_pred %>%
xtabs(~ ConfusionMatrix + True_prediction_total, data = ., addNA = T)
train_pred %>%
xtabs(~ ConfusionMatrix + False_prediction_total, data = ., addNA = T)
#################### OCD
# True prediction_OCD [proba_val_ocd] - TP = 1, FN = 0 / False_prediction_OCD - FN = 1, TP = 0
train_pred_OCD_x <- train_pred %>% filter(Dx == 1)
table(train_pred_OCD_x$ConfusionMatrix) # there's only FN or TP
train_pred_OCD_x <- train_pred_OCD_x %>%
mutate(True_prediction_OCD = ifelse(ConfusionMatrix == 'TP', 1, 0))
train_pred_OCD_x<- train_pred_OCD_x %>%
mutate(False_prediction_OCD = ifelse(ConfusionMatrix == 'FN', 1, 0))
# check
table(train_pred_OCD_x$True_prediction_OCD)
table(train_pred_OCD_x$False_prediction_OCD)
addmargins(xtabs(formula = ~ Site + True_prediction_OCD, data = train_pred_OCD_x)) #
#################### HC
# True prediction_HC [proba_val_hc] - TN = 1, FP =0 / False_prediction_HC - FP = 1, TN = 0
train_pred_HC_x <- train_pred %>% filter(Dx == 0)
table(train_pred_HC_x$ConfusionMatrix) # there's only FP or TN
train_pred_HC_x <- train_pred_HC_x %>%
mutate(True_prediction_HC = ifelse(ConfusionMatrix == 'TN', 1, 0))
train_pred_HC_x <- train_pred_HC_x %>%
mutate(False_prediction_HC = ifelse(ConfusionMatrix == 'FP', 1, 0))
# check
table(train_pred_HC_x$True_prediction_HC)
table(train_pred_HC_x$False_prediction_HC)
addmargins(xtabs(formula = ~ Site + True_prediction_HC, data = train_pred_HC_x)) #
######################
# That is, even when adjusting for covariates (age, sex, site, and mean DTI metrics (FA, MD, AD, RD) , the site variability in classification performance persisted in OCD patients (????2 = 57.194, p< .001) and HCs (????2 = 50.303, p< .001).
# 1. In OCD
# covariate model
# remove Average MD, RD due to vif issue
OCD_model.cov_demo2.brain2.clinical3 <- train_pred_OCD_x %>%
mutate(Med_12 = as.factor(ifelse(Med == 2, 2, ifelse(Med == 1, 1, NA)))) %>%
glm(True_prediction_OCD ~ Age + Sex + AverageFA + AverageAD + Sev + Dur+ Med_12,
family = binomial(link="logit"), data = .)
summary(OCD_model.cov_demo2.brain2.clinical3)
# covariate + site model
OCD_model.cov_demo2.brain2.clinical3_site <- train_pred_OCD_x %>%
mutate(Med_12 = as.factor(ifelse(Med == 2, 2, ifelse(Med == 1, 1, NA)))) %>%
glm(True_prediction_OCD ~ Age + Sex + AverageFA + AverageAD + Sev + Dur+ Med_12 + Site,
family = binomial(link="logit"), data = .)
summary(OCD_model.cov_demo2.brain2.clinical3_site)
# check vif
car::vif(OCD_model.cov_demo2.brain2.clinical3)
car::vif(OCD_model.cov_demo2.brain2.clinical3_site)
# site variability test
anova(OCD_model.cov_demo2.brain2.clinical3, OCD_model.cov_demo2.brain2.clinical3_site, test = 'Chisq') # chisq test : because of logistic regression
# 2. In HC
# covariate model
HC_model.cov_demo2.brain2 <- train_pred_HC_x %>%
glm(True_prediction_HC ~ Age + Sex + AverageFA + AverageAD,
family = binomial(link = 'logit'),data = .)
summary(HC_model.cov_demo2.brain2)
# covariate + site model
HC_model.cov_demo2.brain2_site <- train_pred_HC_x %>%
glm(True_prediction_HC ~ Age + Sex + AverageFA + AverageAD + Site,
family = binomial(link = 'logit'),data = .)
summary(HC_model.cov_demo2.brain2_site)
# site variability test
anova(HC_model.cov_demo2.brain2, HC_model.cov_demo2.brain2_site, test = 'Chisq') # chisq test : because of logistic regression