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ABCD_Harmonization_naturalistic.R
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ABCD_Harmonization_naturalistic.R
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# Author: Dominik Kraft
# This code performs data loading and handling of the ABCD data and subsequently performs
# Combat, Covbat, and Relief harmonization and saves raw/combat/covbat/relief data files for .py scripts
# This code outputs results from the "naturalistic setting" - i.e., harmonization is performed in the whole ABCD sample
# see manuscript for additional information.
rm(list = ls())
set.seed(123)
# set datapath
DATAPATH = "path_to/abcd-data-release-5.0/core"
# set subjectpath
SUBPATH = "path_to_output/controlledharmonization" #set to ABCD_Harmonization_controlled.R output path
date= "_2024-02-28_0956" # get information from ABCD_Harmonization_controlled.R output
# import packages
library(neuroCombat)
library(RELIEF)
library(CovBat)
library(data.table)
library(dplyr)
library(tidyr)
# loading reference subjects for n=500 (modality/harmonization not relevant)
ref_subs <- fread(file.path(SUBPATH, paste0("raw_md_n500_", date, ".csv")), data.table = F)
# loading demographics
demo1<-fread(file.path(DATAPATH, "abcd-general/abcd_p_demo.csv"), data.table = F)
demo2<-fread(file.path(DATAPATH, "abcd-general/abcd_y_lt.csv"), data.table = F)
demo3<-fread(file.path(DATAPATH, "imaging/mri_y_adm_info.csv"), data.table = F)
#select subject, age, sex and scanner site / manufacturer
demo1<-demo1[, c(1:2, 9)]
demo2<-demo2[, c(1:2, 9)]
demo3<-demo3[, c(1:2, 5:6)]
#merge demos
demo<-Reduce(function(x,y) merge(x, y, by= c("src_subject_id", "eventname")),
list( demo1, demo2, demo3))
# perform some data wrangling, re-naming etc. on demo
demo<-rename(demo, subject = src_subject_id)
demo$eventname <- gsub("(?<=^|_)([a-z])", "\\U\\1", demo$eventname, perl=TRUE)
demo$eventname<- gsub("_", "", demo$eventname)
demo$subject<- gsub("_", "", demo$subject)
demo<-rename(demo, session = eventname)
demo<-rename(demo, age = interview_age)
demo<-rename(demo,sex = demo_sex_v2)
demo<-rename(demo, site = mri_info_deviceserialnumber)
demo<-rename(demo, manufacturer = mri_info_manufacturersmn)
demo<- demo %>%
group_by(subject) %>%
fill(sex, .direction = "downup") %>%
ungroup
## select only baseline data
baseline <- demo[demo$session == "BaselineYear1Arm1", ]
# select only male / female data
baseline <- baseline[baseline$sex %in% list(1,2),]
# define function for loading and processing imaging data
process_data <- function(file_path) {
# Load data
data <- fread(file_path, data.table = FALSE)
# Clean up eventname column
data$eventname <- gsub("(?<=^|_)([a-z])", "\\U\\1", data$eventname, perl=TRUE)
data$eventname <- gsub("_", "", data$eventname)
# Rename columns
data <- rename(data, subject = src_subject_id)
data$subject <- gsub("_", "", data$subject)
data <- rename(data, session = eventname)
# Filter rows for the desired session
data <- data[data$session == "BaselineYear1Arm1", ]
# Ignore columns with "all" for fa and md
data <- data[, !grepl("all", names(data))]
# Omit rows with NaN values
data <- na.omit(data)
return(data)
}
# fa = fractional anisotropy, md = mean diffusivity
fa <- process_data(file.path(DATAPATH, "imaging/mri_y_dti_fa_is_at.csv"))
md <- process_data(file.path(DATAPATH, "imaging/mri_y_dti_md_is_at.csv"))
## merge demo and brain dataframes (fa, md)
df1<-Reduce(function(x,y) merge(x, y, by= c("subject", "session")),
list(baseline,fa))
df2<-Reduce(function(x,y) merge(x, y, by= c("subject", "session")),
list(baseline,md))
# define harmonization function
harmonization_function <- function(data_frame, output_prefix, size, write_output=TRUE) {
# Convert 'sex' column to factor
#data_frame$sex <- as.factor(data_frame$sex)
# Select covariates
covar <- data_frame[, c(1:6)]
# Extract 'site' for batch
batch <- covar$site
# Extract data for fa/md
data <- data_frame[, 7:43]
# Transform data
data <- t(data)
# Define covariates
modcovar <- model.matrix(~ as.factor(sex) + age, data = covar)
# Combat harmonization
combat_result <- neuroCombat(dat = data, batch = batch, mod = modcovar)
datcombat <- t(as.data.frame(combat_result$dat.combat))
combat_harmonized <- cbind(covar, datcombat)
# Relief harmonization
relief_result <- relief(dat = data, batch = batch, mod = modcovar)
datrelief <- t(as.data.frame(relief_result$dat.relief))
relief_harmonized <- cbind(covar, datrelief)
# CovBat harmonization
covbat_result <- covbat(dat = data, bat = batch, mod = modcovar)
datcovbat <- t(as.data.frame(covbat_result$dat.covbat))
covbat_harmonized <- cbind(covar, datcovbat)
result_list <- list(
combat = combat_harmonized,
covbat = covbat_harmonized,
relief = relief_harmonized)
# Write tables
if (write_output) {
write.table(data_frame, file = paste0("raw_", output_prefix, "_n", size, "_", date, ".csv"), sep = ",", row.names = FALSE)
write.table(combat_harmonized, file = paste0("combat_", output_prefix,"_n", size, "_", date, ".csv"), sep = ",", row.names = FALSE)
write.table(relief_harmonized, file = paste0("relief_", output_prefix, "_n", size, "_",date, ".csv"), sep = ",", row.names = FALSE)
write.table(covbat_harmonized, file = paste0("covbat_", output_prefix, "_n", size, "_",date, ".csv"), sep = ",", row.names = FALSE)
}
return(result_list)
}
# stepwise exclusion of i-smallest scanning sites
# note that this code does not return full sample without excluded sites
for (i in 1:9) {
cat("----- deleting", i, "sites ----")
print(sort(table(df1$site))[1:i])
excl <- names(sort(table(df1$site))[1:i])
df1_temp <- df1[!(df1$site %in% excl), ]
df2_temp <- df2[!(df2$site %in% excl), ]
stopifnot(identical(sort(df1_temp$subject), sort(df2_temp$subject)))
# perform harmonization without subsampling, i.e., on all sites and subjects at baseline
fa_result_real = harmonization_function(df1_temp, "fa", nrow(df1_temp), write_output = FALSE)
# use reference subjects for benchmarking (i.e., 4000 subjects that were used in simulated scenario)
# FA
fa_result_real500 <- lapply(fa_result_real, function(df) {
df_filtered <- df %>%
filter(subject %in% ref_subs$subject) %>%
arrange(subject)
stopifnot(identical(df_filtered$subject, ref_subs$subject))
return(df_filtered)
})
# save FA
lapply(names(fa_result_real500), function(name) {
write.table(fa_result_real500[[name]], file = paste0(name, "_fa_harmonAll_excluded_sites_n=", i, ".csv"), sep = ",", row.names = FALSE)
})
# MD
md_result_real = harmonization_function(df2_temp, "md", nrow(df2_temp), write_output = FALSE)
# choose the same 500 subjects and sites as done above
md_result_real500 <- lapply(md_result_real, function(df) {
df_filtered <- df %>%
filter(subject %in% ref_subs$subject) %>%
arrange(subject)
stopifnot(identical(df_filtered$subject, ref_subs$subject))
return(df_filtered)
})
# save
lapply(names(md_result_real500), function(name) {
write.table(md_result_real500[[name]], file = paste0(name, "_md_harmonAll_excluded_sites_n=", i, ".csv"), sep = ",", row.names = FALSE)
})
}