Location on server: /space/chen-syn01/1/data/cinliu/plots/UKB_cortex_result_brainmaps
(code is with in folder with images)
Embedded Files
Brain Plot
Brain Plot
Step1: Basic set up
rm(list=ls())Step1: Basic set up
setwd('/Users/nini/Desktop/2021lab/Hao/brainplot/')library(ggsegChen)library(ggseg)library(dplyr)library(readxl) #reading excel library(RColorBrewer) #for colors
Step2: Import file
#input csv file UKB_cortex_result ← read.csv("~/Desktop/2021lab/Hao/brainplot/UKB_cortex_result.txt", sep="")head(UKB_cortex_result)df ← UKB_cortex_result #save "UKB_cortex_result" as dfStep2: Import file
This file looks something like this:
This data set has 18 different "inv" (in the picture above we can only see "1p22.1")
We want to plot the "p_global" for each "inv", thus we should end up with 18 graphs in the end.
Step3: Adjust data
Step3: Adjust data
Change the name of the regions on the data frame to match the region name formate of ggseg.
You can run the code ggsegChen::chenTh or ggsegChen::chenAr to see the proper spelling of the region names ggsegChen uses.
Note: Make sure to check there are no accidental spaces before or after of the region names, ggsegchen is not that forgiving if there is like an accidental space after a region name it will not recognize it and fail to gedf$region <- gsub("ChenA_bh_", "\\1", df$roi) #make a new col called region that is a copy of roi and modify "region" to match the ggseg region names. can run #ggsegChen::chenTh or #ggsegChen::chenAr to see the proper spelling of the region namesdf$region <- gsub("_area", "", df$region)df[df$region == "superiortemporal",7] <- "superior temporal"df[df$region == "parsopercularis",7] <- "pars opercularis,subcentral"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "posterolateraltemporal",7] <- "posterolateral temporal"df[df$region == "motorpremotor",7] <- "motor-premotor"df[df$region == "dorsomedialfrontal",7] <- "dorsomedial frontal"df[df$region == "anteromedialtemporal",7] <- "anteromedial temporal"
df$region <- gsub("ChenT_bh_", "\\1", df$region)df$region <- gsub("_thickness", "", df$region)df[df$region == "ventromedialoccipital",7] <- "ventromedial occipital"df[df$region == "ventralfrontal",7] <- "ventral frontal"df[df$region == "temporalpole",7] <- "temporal pole"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "motor_premotor_SMA",7] <- "motor-premotor-supplementary motor area"df[df$region == "middletemporal",7] <- "middle temporal"df[df$region == "medialtemporal",7] <- "medial temporal"df[df$region == "medialprefrontal",7] <- "medial prefrontal"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"
Calculate the negative log value (so we can later plot using -log10(p)
Get list of inversion names. There are 18 names in this case
Step4: Build loop and generate graphs
Step4: Build loop and generate graphs
To make a ggsegChen brain plot it is actually very simple. The package only requires 2 things to be able to build a graph.
Specify if you are using brain thickness or brain area
Give it data that has 2 column of information: region name and value assigned to the region.
However in this particular case we want to generate a brain plot for each "inv" region in this case, so we need to build a loop through the list of "inv_names".
In this dataset it is every 26 rows there is a new name for "inv" so we will set 1 and 26 as our starting number a <- 1 b <- 26
make loop for (i in 1:length(inv_names))
make a separate data frame that only has the first 26 rows dfs <- df[a:b,] dfs is the new dataframe we will be working with from now on
pull out the "inv" name for later use inv <- dfs$inv[1]
for (i in 1:length(inv_names)) { print(i) #I want it to print out the i just to keep track of where we are in the loop dfs <- df[a:b,]
Making the area brainplot
Pull out the area data area <- dfs[1:12,]
Make the plot
plot1 <- area%>%
ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)))+
labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
ggsave(paste0(inv,"_area.png"), width = 10, height = 3)
Code break down
plot1 <- area%>% chains the data to the 4 lines of code that follows it
ggseg()
Specify we want to use the Chen Area map atlas = "chenAr"
Specify the value we want to plot mapping = aes(fill = as.numeric(neg_log_p_global)))+
Labels labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
the title of the plot title paste0(inv,"_area.png")
the title of the color key fill="-log10(p_global)
Specify the color scale scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
Save the plot using ggsave, saving it as 10x 3 in this case ggsave(paste0(inv,"_area.png"), width = 10, height = 3)
Repeat similar setps but this time do it for thickness (which is the next 26 line of data in dfs)
thickness <- dfs[13:24,]
thickness%>%
ggseg(atlas = "chenTh",mapping = aes(fill = as.numeric(neg_log_p_global)))+
labs(title = paste0(inv,"_thickness.png"),fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
ggsave(paste0(inv,"_thickness.png"), width = 10, height = 3)
In the end add 26 to "a" and "b" to move to the new net of "inv" data. Close the loop in the end.
a <- a+26
b <- b+26
}
Sample result plot:
Full code brainplot_p_global.R
rm(list=ls())
setwd('/Users/nini/Desktop/2021lab/Hao/brainplot/')library(ggsegChen)library(ggseg)library(dplyr)library(readxl) #reading excel library(RColorBrewer) #for colors
#input csv file UKB_cortex_result <- read.csv("~/Desktop/2021lab/Hao/brainplot/UKB_cortex_result.txt", sep="")head(UKB_cortex_result)
df <- UKB_cortex_resultdf$region <- gsub("ChenA_bh_", "\\1", df$roi) #make a new col called region that is a copy of roi and modify "region" to match the ggseg region names. can run #ggsegChen::chenTh or #ggsegChen::chenAr to see the proper spelling of the region namesdf$region <- gsub("_area", "", df$region)df[df$region == "superiortemporal",7] <- "superior temporal"df[df$region == "parsopercularis",7] <- "pars opercularis,subcentral"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "posterolateraltemporal",7] <- "posterolateral temporal"df[df$region == "motorpremotor",7] <- "motor-premotor"df[df$region == "dorsomedialfrontal",7] <- "dorsomedial frontal"df[df$region == "anteromedialtemporal",7] <- "anteromedial temporal"
df$region <- gsub("ChenT_bh_", "\\1", df$region)df$region <- gsub("_thickness", "", df$region)df[df$region == "ventromedialoccipital",7] <- "ventromedial occipital"df[df$region == "ventralfrontal",7] <- "ventral frontal"df[df$region == "temporalpole",7] <- "temporal pole"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "motor_premotor_SMA",7] <- "motor-premotor-supplementary motor area"df[df$region == "middletemporal",7] <- "middle temporal"df[df$region == "medialtemporal",7] <- "medial temporal"df[df$region == "medialprefrontal",7] <- "medial prefrontal"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"
df$neg_log_p_global <- -log10(df$p_global)
inv_names <- unique(UKB_cortex_result$inv)a <- 1b <- 26
for (i in 1:length(inv_names)) { print(i) dfs <- df[a:b,] inv <- dfs$inv[1] area <- dfs[1:12,] plot1 <- area%>% ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)))+ labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+ scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent") ggsave(paste0(inv,"_area.png"), width = 10, height = 3) thickness <- dfs[13:24,] thickness%>% ggseg(atlas = "chenTh",mapping = aes(fill = as.numeric(neg_log_p_global)))+ labs(title = paste0(inv,"_thickness.png"),fill="-log10(p_global)")+ scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent") ggsave(paste0(inv,"_thickness.png"), width = 10, height = 3) a <- a+26 b <- b+26}
setwd('/Users/nini/Desktop/2021lab/Hao/brainplot/')library(ggsegChen)library(ggseg)library(dplyr)library(readxl) #reading excel library(RColorBrewer) #for colors
#input csv file UKB_cortex_result <- read.csv("~/Desktop/2021lab/Hao/brainplot/UKB_cortex_result.txt", sep="")head(UKB_cortex_result)
df <- UKB_cortex_resultdf$region <- gsub("ChenA_bh_", "\\1", df$roi) #make a new col called region that is a copy of roi and modify "region" to match the ggseg region names. can run #ggsegChen::chenTh or #ggsegChen::chenAr to see the proper spelling of the region namesdf$region <- gsub("_area", "", df$region)df[df$region == "superiortemporal",7] <- "superior temporal"df[df$region == "parsopercularis",7] <- "pars opercularis,subcentral"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "posterolateraltemporal",7] <- "posterolateral temporal"df[df$region == "motorpremotor",7] <- "motor-premotor"df[df$region == "dorsomedialfrontal",7] <- "dorsomedial frontal"df[df$region == "anteromedialtemporal",7] <- "anteromedial temporal"
df$region <- gsub("ChenT_bh_", "\\1", df$region)df$region <- gsub("_thickness", "", df$region)df[df$region == "ventromedialoccipital",7] <- "ventromedial occipital"df[df$region == "ventralfrontal",7] <- "ventral frontal"df[df$region == "temporalpole",7] <- "temporal pole"df[df$region == "superiorparietal",7] <- "superior parietal"df[df$region == "motor_premotor_SMA",7] <- "motor-premotor-supplementary motor area"df[df$region == "middletemporal",7] <- "middle temporal"df[df$region == "medialtemporal",7] <- "medial temporal"df[df$region == "medialprefrontal",7] <- "medial prefrontal"df[df$region == "inferiorparietal",7] <- "inferior parietal"df[df$region == "dorsolateralprefrontal",7] <- "dorsolateral prefrontal"
df$neg_log_p_global <- -log10(df$p_global)
inv_names <- unique(UKB_cortex_result$inv)a <- 1b <- 26
for (i in 1:length(inv_names)) { print(i) dfs <- df[a:b,] inv <- dfs$inv[1] area <- dfs[1:12,] plot1 <- area%>% ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)))+ labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+ scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent") ggsave(paste0(inv,"_area.png"), width = 10, height = 3) thickness <- dfs[13:24,] thickness%>% ggseg(atlas = "chenTh",mapping = aes(fill = as.numeric(neg_log_p_global)))+ labs(title = paste0(inv,"_thickness.png"),fill="-log10(p_global)")+ scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent") ggsave(paste0(inv,"_thickness.png"), width = 10, height = 3) a <- a+26 b <- b+26}
Additional brain output positioning
Additional brain output positioning
Very useful papaer that provides instruction on how to change brain positioning and other things in ggseg:
Mowinckel, A. M., & Vidal-Piñeiro, D. (2020). Visualization of Brain Statistics With R Packages ggseg and ggseg3d. Advances in Methods and Practices in Psychological Science, 466–483. https://doi.org/10.1177/2515245920928009
Direct link to site: https://journals.sagepub.com/doi/full/10.1177/2515245920928009
Direct link to pdf: https://journals.sagepub.com/doi/pdf/10.1177/2515245920928009
Here are just some sample of different positions:
Position "stacked"
area <- dfs[1:12,]
plot1 <- area%>%
ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)),position = "stacked")+
labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
Position "stacked", theme dark
plot1 <- area%>%
ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)),position = "stacked")+
labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")+
theme_dark()
Position "stacked", Left hemisphere (can do the same for right)
plot1 <- area%>%
ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)),position = "stacked", hemisphere = "left")+
labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
Position "stacked", medial (can do the same for lateral view)
plot1 <- area%>%
ggseg(atlas = "chenAr",mapping = aes(fill = as.numeric(neg_log_p_global)),position = "stacked", view = "medial")+
labs(title =paste0(inv,"_area.png"), fill="-log10(p_global)")+
scale_fill_gradientn(colours = c("deepskyblue2","royalblue4"),na.value = "transparent")
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