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ROSMAPanalysisSynapse.Rmd
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---
title: "ROSMAP specific analysis"
output:
html_document:
df_print: paged
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
```{r}
library(dplyr)
library(data.table)
library(ggplot2)
library(rms)
library(ggrepel)
library(grid)
library(gridExtra)
library(cowplot)
```
We've loaded in the necessary libraries for the ROSMAP specific analysis, so let's get the marker list for prefrontal regions and the rCTPs calculated on the ROSMAP dataset using this marker list and the necessary metadata associated with the cohort.
```{r}
marker <- c("subclass_MTGCgG_derived_common_final")
setwd('./commonMarkerLists')
#get marker list
subclass_MTGCgG_derived_common_final <- readRDS(paste0(marker, '.rds'))
setwd('../')
#get MGP, aka the rCTPs
mgp_name <-"mgp_ROSMAP"
setwd('./mgpResults_subclass_MTGCgG_derived')
ROS_mgp <- readRDS(paste0(mgp_name, ".rds"))
ROSMGP <- ROS_mgp$model
setwd('../')
#get the metadata
setwd('./cohortMetadata')
ROS_meta <- readRDS("ROSmasterCOMPLETE.rds")
#get the key to convert the subject ids to the ones associated with more metadata
ROSID <- read.csv(file = 'ROSMAP_IDkey.csv', stringsAsFactors = F)
setwd('../')
#get cognitive scores at last visit
last_cog_scores <- filter(ROS_meta, !is.na(cogn_globaln_lv))
last_cog_scores <- last_cog_scores %>% dplyr::select("projid", "cogn_globaln_lv" )
last_cog_scores <- na.omit(last_cog_scores)
#select the following pathologies for calculating cognitive residuals
pathology_names <- c("msex", "pmi", "age_death", "tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n", "plaq_d", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_typ", "educ",
"apoe4d", "apoe2d")
pathology_vars <- ROS_meta %>% dplyr::select("projid", all_of(pathology_names))
pathology_vars <- na.omit(pathology_vars)
combined_data <- inner_join(last_cog_scores, pathology_vars)
conversion <- data.frame("subject" = ROSID$projid, "projid" = ROSID$rnaseq_id)
merged <- merge(conversion, ROSMGP, by="projid")
merged <- setnames(merged, old = c('projid','subject'), new = c('subject','projid'))
relevant_merge <- data.frame("projid"= merged$projid, "SST" = merged$SST, "IT"= merged$IT)
```
Now we've got a dataframe of our subjects along with their values for all the pathology variables relevant for calculating cognitive residuals. It's time to calculate the cognitive residuals.
```{r}
mod_info <- merge(combined_data, relevant_merge, by="projid")
model_names <- c("BASELINE", "APOE4D","APOE2D", "IT", "APOE4D.IT", "APOE2D.IT", "SST", "APOE4D.SST", "IT.SST","APOE2D.SST" )
for(model in model_names){
base_vars <- c("msex", "pmi", "age_death", "tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n", "plaq_d", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_typ", "educ")
if(model == "BASELINE"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(base_vars, sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE2D"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe2d"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars,"IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D.IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d + IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE2D.IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars,"apoe2d + IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "SST"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D.SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d + SST"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "IT.SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "IT + SST"), sep="",collapse=" + ")," )",sep="")
}
else{
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe2d + SST"), sep="",collapse=" + ")," )",sep="")
}
mod_cog <- ols(eval(parse(text=form)), data=mod_info, x=T, y=T)
mod_for_anova <- lm(eval(parse(text=form)), data= mod_info)
validation_data <- validate(mod_cog, method=".632", B=100)
setwd('./ROSMAPcogRes')
saveRDS(mod_cog, paste0(model, "cog_res_model.rds"))
assign(paste0(model, "cog_ANOVA_model"), mod_for_anova)
setwd('../')
print(model)
mod_info[paste0(model, "cog_res")] <- resid(mod_cog)
if(model=="BASELINE"){
model_data <- data.frame("model" = model, "r2" = mod_cog$stats['R2'], "optimisim" = validation_data[1,4], "corrR2" =validation_data[1,5], "p.val" = NA)
}
else{
significance <- (anova(BASELINEcog_ANOVA_model, mod_for_anova)[6])[2,1]
temp = data.frame("model" = model, "r2" = mod_cog$stats['R2'], "optimisim" = validation_data[1,4], "corrR2" =validation_data[1,5], "p.val" = significance)
model_data <- rbind(model_data, temp)
}
}
model_data$FDR <- p.adjust(model_data$p.val, method="fdr")
model_data$significant <- ifelse(model_data$FDR < 0.05, "**", "")
model_data$bonf <- p.adjust(model_data$p.val, method="bonf")
model_data$significant_bonf <- ifelse(model_data$bonf < 0.05, "***", "")
```
Let's calculate the association between cognitive residuals in ROSMAP and rCTPs.
```{r}
cell_type_names <- (names(subclass_MTGCgG_derived_common_final))
pathology_names <- c("tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n_sqrt", "plaq_d_sqrt", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_any", "cogn_globaln_lv",
"cogn_global_random_slope", "BASELINEcog_res")
covars <- c("msex","age_death","pmi")
new_relevant_merge <- merged[, c("projid", cell_type_names )]
cell_type_names <- make.names(cell_type_names)
colnames(new_relevant_merge) <- c("projid", cell_type_names)
new_mod_info <- merge(combined_data, new_relevant_merge, by="projid")
new_mod_info <- merge(new_mod_info, ROS_meta)
new_mod_info$BASELINEcog_res <- mod_info$BASELINEcog_res
model.data <- new_mod_info
results <- sapply(cell_type_names,function(celltype) {
sapply(pathology_names, function(pathology) {
if(pathology =="cogn_global_random_slope" |pathology =="cogn_globaln_lv" | pathology =="BASELINEcog_res" ){
covars <-c("educ", "age_bl", "msex", "age_death", "pmi")
}
form <- as.formula(paste0(celltype,"~",pathology," + ",paste0(covars,collapse=" + ")))
if(pathology == 'LOAD'){
model <- lm(data=model.data,form)
p <- anova(model)[pathology,5]
beta <- coef(model)['LOAD1']
}
else{
model <- lm(data=model.data,form)
p <- anova(model)[pathology,5]
beta <- coef(model)[pathology]
}
n <- nrow(model$model)
return(c(celltype,pathology,beta, p,n))
})
})
results <- as.data.frame(matrix(results,ncol=5,byrow = T),stringsAsFactors = F)
names(results) <- c("celltype","pathology","beta","p","n")
results <- within(results,{
p <- as.numeric(p)
beta <- as.numeric(beta)
n <- as.numeric(n)})
results$bonfp <- p.adjust(results$p, method="bonferroni")
results$fdr <- p.adjust(results$p, method="fdr")
results$signedFDR <- -log10(results$fdr) *(sign(results$beta))
setwd('./ROSMAP_spec')
saveRDS(results, "ROS_results.rds")
setwd('../')
```
Let's plot.
```{r}
setwd('./subclassMeta')
subclass_meta <- read.csv('subclass_meta.txt')
setwd('../')
subclass_meta$celltype <- make.names(subclass_meta$AIBS_subclass_label)
results <- merge(subclass_meta, results)
results$class <- as.factor(results$AIBS_class_label)
results$class <- factor(results$AIBS_class_label, levels = c("GABAergic", "Glutamatergic",
"Non-neuronal"))
results <- arrange(results, class)
results$pathology <- as.factor(results$pathology)
results$pathology <- factor(results$pathology, levels = c("amyloid_sqrt", "tangles_sqrt",
"tdp_stage4",
"braaksc", "dlbdx", "plaq_d_sqrt",
"plaq_n_sqrt", "ci_num2_gct",
"cvda_4gp2", "hspath_any",
"caa_4gp","cogn_globaln_lv",
"cogn_global_random_slope",
"BASELINEcog_res"))
results <- arrange(results, class)
heatmap <- ggplot(results, aes(celltype, pathology, fill= signedFDR))+
theme_minimal() + geom_tile() +
facet_grid(~AIBS_class_label, scale = 'free_x', space = 'free_x') +
scale_fill_gradient2(low="darkblue", high="darkgreen", guide="colorbar") +
geom_text(data=subset(results,fdr<0.05), aes(label="**"), color ="white") +
theme_minimal() +
theme(axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x = element_text(angle = 90),
axis.ticks.x=element_blank())
model_data$model <- gsub(".", '+', model_data$model, fixed = T)
model_data$model <- factor(model_data$model, levels = rev(c("BASELINE", "APOE4D",
"APOE2D", "IT",
"APOE4D+IT","APOE2D+IT","SST",
"APOE2D+SST",
"APOE4D+SST", "IT+SST")))
model_data <- arrange(model_data, model)
model_data$range <- model_data$r2 - model_data$r2[length(model_data$r2)]
model_data$percent <- as.numeric(format(round(model_data$range*100, 2), nsmall = 2))
model_data$raw_percent <- as.numeric(format(round(model_data$r2*100, 2), nsmall = 2))
#remove APOE2D info
#model_data <- model_data[-c(3, 5, 8),]
model_plot = model_data %>%
ggplot(aes(x = model, y = percent ,fill = "#E6E6FA")) +
scale_fill_manual(values = "#967bb6") +
scale_y_continuous(expand = c(0,0))+
geom_bar(stat = "identity", show.legend = FALSE) +
ylab(paste0('Additional % Variance Explained \n Beyond Baseline ', format(round(model_data$r2[length(model_data$r2)]*100)), '% R2')) +
xlab('Model for Residual Cogniton') + coord_flip()
print(heatmap)
print(model_plot)
#generate SST/IT scatter plots for cognitive slope, amyloid, and tangles
SST_amyloid <- ggplot(data=model.data, aes(y=amyloid_sqrt, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
SST_cogn <- ggplot(data=model.data, aes(y=cogn_globaln_lv, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
SST_tangles <- ggplot(data=model.data, aes(y=tangles_sqrt, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_cogn <- ggplot(data=model.data, aes(y=cogn_globaln_lv, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_tangles <- ggplot(data=model.data, aes(y=tangles_sqrt, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_amyloid <- ggplot(data=model.data, aes(y=amyloid_sqrt, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
mid_plots = plot_grid(SST_cogn, SST_amyloid,
SST_tangles,IT_cogn,
IT_amyloid, IT_tangles, nrow = 2, ncol = 3,
axis = 'l', align = 'v', labels = c('B', 'C', 'D', 'E', 'F', 'G'))
full_plot = plot_grid(heatmap, mid_plots, model_plot, nrow = 3 , rel_heights = c(5,4, 5),
labels = c('A', '', 'H'))
setwd('./figures')
saveRDS(full_plot, "figure4.rds")
pdf(file = "figure4.pdf",
width = 10, # The width of the plot in inches
height = 12)
print(full_plot)
dev.off()
setwd('../')
print(full_plot)
```
Let's see what results we get when we remove the SST gene from the marker list defining SST cell-type proportions.
```{r}
marker <- c("subclass_MTGCgG_derived_no_SST")
setwd('./commonMarkerLists')
#get marker list
subclass_MTGCgG_derived_no_SST <- readRDS(paste0(marker, '.rds'))
setwd('../')
#get MGP, aka the rCTPs
mgp_name <-"mgp_ROSMAP"
setwd('./mgpResults_subclass_MTGCgG_derived_no_SST')
ROS_mgp <- readRDS(paste0(mgp_name, ".rds"))
ROSMGP <- ROS_mgp$model
setwd('../')
#get the metadata
setwd('./cohortMetadata')
ROS_meta <- readRDS("ROSmasterCOMPLETE.rds")
#get the key to convert the subject ids to the ones associated with more metadata
ROSID <- read.csv(file = 'ROSMAP_IDkey.csv', stringsAsFactors = F)
setwd('../')
#get cognitive scores at last visit
last_cog_scores <- filter(ROS_meta, !is.na(cogn_globaln_lv))
last_cog_scores <- last_cog_scores %>% dplyr::select("projid", "cogn_globaln_lv" )
last_cog_scores <- na.omit(last_cog_scores)
#select the following pathologies for calculating cognitive residuals
pathology_names <- c("msex", "pmi", "age_death", "tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n", "plaq_d", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_typ", "educ",
"apoe4d", "apoe2d")
pathology_vars <- ROS_meta %>%dplyr::("projid", all_of(pathology_names))
pathology_vars <- na.omit(pathology_vars)
combined_data <- inner_join(last_cog_scores, pathology_vars)
conversion <- data.frame("subject" = ROSID$projid, "projid" = ROSID$rnaseq_id)
merged <- merge(conversion, ROSMGP, by="projid")
merged <- setnames(merged, old = c('projid','subject'), new = c('subject','projid'))
relevant_merge <- data.frame("projid"= merged$projid, "SST" = merged$SST, "IT"= merged$IT)
mod_info <- merge(combined_data, relevant_merge, by="projid")
model_names <- c("BASELINE", "APOE4D","APOE2D", "IT", "APOE4D.IT", "APOE2D.IT", "SST", "APOE4D.SST", "IT.SST","APOE2D.SST" )
for(model in model_names){
base_vars <- c("msex", "pmi", "age_death", "tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n", "plaq_d", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_typ", "educ")
if(model == "BASELINE"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(base_vars, sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE2D"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe2d"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars,"IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D.IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d + IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE2D.IT"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars,"apoe2d + IT"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "SST"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "APOE4D.SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe4d + SST"), sep="",collapse=" + ")," )",sep="")
}
else if (model == "IT.SST"){
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "IT + SST"), sep="",collapse=" + ")," )",sep="")
}
else{
form <- paste("as.formula( mod_info$cogn_globaln_lv ~ ",paste(append(base_vars, "apoe2d + SST"), sep="",collapse=" + ")," )",sep="")
}
mod_cog <- ols(eval(parse(text=form)), data=mod_info, x=T, y=T)
mod_for_anova <- lm(eval(parse(text=form)), data= mod_info)
validation_data <- validate(mod_cog, method=".632", B=100)
setwd('./ROSMAPcogRes')
saveRDS(mod_cog, paste0(model, "cog_res_model.rds"))
assign(paste0(model, "cog_ANOVA_model"), mod_for_anova)
setwd('../')
print(model)
mod_info[paste0(model, "cog_res")] <- resid(mod_cog)
if(model=="BASELINE"){
model_data_noSST <- data.frame("model" = model, "r2" = mod_cog$stats['R2'], "optimisim" = validation_data[1,4], "corrR2" =validation_data[1,5], "p.val" = NA)
}
else{
significance <- (anova(BASELINEcog_ANOVA_model, mod_for_anova)[6])[2,1]
temp = data.frame("model" = model, "r2" = mod_cog$stats['R2'], "optimisim" = validation_data[1,4], "corrR2" =validation_data[1,5], "p.val" = significance)
model_data_noSST <- rbind(model_data_noSST, temp)
}
}
model_data_noSST$FDR <- p.adjust(model_data_noSST$p.val, method="fdr")
model_data_noSST$significant <- ifelse(model_data_noSST$FDR < 0.05, "**", "")
model_data_noSST$bonf <- p.adjust(model_data_noSST$p.val, method="bonf")
model_data_noSST$significant_bonf <- ifelse(model_data_noSST$bonf < 0.05, "***", "")
```
Let's calculate the association between cognitive residuals in ROSMAP and rCTPs without the SST gene.
```{r}
cell_type_names <- (names(subclass_MTGCgG_derived_no_SST))
pathology_names <- c("tangles_sqrt",
"amyloid_sqrt", "tdp_stage4", "braaksc",
"dlbdx", "plaq_n_sqrt", "plaq_d_sqrt", "ci_num2_gct",
"cvda_4gp2", "caa_4gp", "hspath_any", "cogn_globaln_lv",
"cogn_global_random_slope", "BASELINEcog_res")
covars <- c("msex","age_death","pmi")
new_relevant_merge <- merged[, c("projid", cell_type_names )]
cell_type_names <- make.names(cell_type_names)
colnames(new_relevant_merge) <- c("projid", cell_type_names)
new_mod_info <- merge(combined_data, new_relevant_merge, by="projid")
new_mod_info <- merge(new_mod_info, ROS_meta)
new_mod_info$BASELINEcog_res <- mod_info$BASELINEcog_res
model.data <- new_mod_info
results <- sapply(cell_type_names,function(celltype) {
sapply(pathology_names, function(pathology) {
if(pathology =="cogn_global_random_slope" |pathology =="cogn_globaln_lv" | pathology =="BASELINEcog_res" ){
covars <-c("educ", "age_bl", "msex", "age_death", "pmi")
}
form <- as.formula(paste0(celltype,"~",pathology," + ",paste0(covars,collapse=" + ")))
if(pathology == 'LOAD'){
model <- lm(data=model.data,form)
p <- anova(model)[pathology,5]
beta <- coef(model)['LOAD1']
}
else{
model <- lm(data=model.data,form)
p <- anova(model)[pathology,5]
beta <- coef(model)[pathology]
}
n <- nrow(model$model)
return(c(celltype,pathology,beta, p,n))
})
})
results <- as.data.frame(matrix(results,ncol=5,byrow = T),stringsAsFactors = F)
names(results) <- c("celltype","pathology","beta","p","n")
results <- within(results,{
p <- as.numeric(p)
beta <- as.numeric(beta)
n <- as.numeric(n)})
results$bonfp <- p.adjust(results$p, method="bonferroni")
results$fdr <- p.adjust(results$p, method="fdr")
results$signedFDR <- -log10(results$fdr) *(sign(results$beta))
```
Let's plot.
```{r}
setwd('./subclassMeta')
subclass_meta <- read.csv('subclass_meta.txt')
setwd('../')
subclass_meta$celltype <- make.names(subclass_meta$AIBS_subclass_label)
results <- merge(subclass_meta, results)
results$class <- as.factor(results$AIBS_class_label)
results$class <- factor(results$AIBS_class_label, levels = c("GABAergic", "Glutamatergic",
"Non-neuronal"))
results <- arrange(results, class)
results$pathology <- as.factor(results$pathology)
results$pathology <- factor(results$pathology, levels = c("amyloid_sqrt", "tangles_sqrt",
"tdp_stage4",
"braaksc", "dlbdx", "plaq_d_sqrt",
"plaq_n_sqrt", "ci_num2_gct",
"cvda_4gp2", "hspath_any",
"caa_4gp","cogn_globaln_lv",
"cogn_global_random_slope",
"BASELINEcog_res"))
results <- arrange(results, class)
heatmap <- ggplot(results, aes(celltype, pathology, fill= signedFDR))+
theme_minimal() + geom_tile() +
facet_grid(~AIBS_class_label, scale = 'free_x', space = 'free_x') +
scale_fill_gradient2(low="darkblue", high="darkgreen", guide="colorbar") +
geom_text(data=subset(results,bonfp<0.05), aes(label="*"), color ="white") +
theme_minimal() +
theme(axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks.y=element_blank(),
axis.text.x = element_text(angle = 90),
axis.ticks.x=element_blank())
model_data_noSST$model <- gsub(".", '+', model_data_noSST$model, fixed = T)
model_data_noSST$model <- factor(model_data_noSST$model, levels = rev(c("BASELINE", "APOE4D",
"APOE2D", "IT",
"APOE4D+IT","APOE2D+IT","SST",
"APOE2D+SST",
"APOE4D+SST", "IT+SST")))
model_data_noSST <- arrange(model_data_noSST, model)
model_data_noSST$range <-
model_data_noSST$r2 - model_data_noSST$r2[length(model_data_noSST$r2)]
model_data_noSST$percent <-
as.numeric(format(round(model_data_noSST$range*100, 2), nsmall = 2))
model_data_noSST$raw_percent <-
as.numeric(format(round(model_data_noSST$r2*100, 2), nsmall = 2))
#remove APOE2D info
model_data_noSST <- model_data_noSST[-c(3, 5, 8),]
model_plot = model_data_noSST %>%
ggplot(aes(x = model, y = percent ,fill = "#E6E6FA")) +
scale_fill_manual(values = "#967bb6") +
scale_y_continuous(expand = c(0,0))+
geom_bar(stat = "identity", show.legend = FALSE) +
ylab(paste0('Additional % Variance Explained \n Beyond Baseline ', format(round(model_data_noSST$r2[length(model_data_noSST$r2)]*100)), '% R2')) +
xlab('Model for Residual Cogniton') + coord_flip()
print(heatmap)
print(model_plot)
#generate SST/IT scatter plots for cognitive slope, amyloid, and tangles
SST_amyloid <- ggplot(data=model.data, aes(y=amyloid_sqrt, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
SST_cogn <- ggplot(data=model.data, aes(y=cogn_globaln_lv, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
SST_tangles <- ggplot(data=model.data, aes(y=tangles_sqrt, x=SST)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_cogn <- ggplot(data=model.data, aes(y=cogn_globaln_lv, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_tangles <- ggplot(data=model.data, aes(y=tangles_sqrt, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
IT_amyloid <- ggplot(data=model.data, aes(y=amyloid_sqrt, x=IT)) + theme_minimal()+ geom_point() +
geom_smooth(method=lm, se =F)
mid_plots = plot_grid(SST_cogn, SST_amyloid,
SST_tangles,IT_cogn,
IT_amyloid, IT_tangles, nrow = 2, ncol = 3,
axis = 'l', align = 'v', labels = c('B', 'C', 'D', 'E', 'F', 'G'))
full_plot = plot_grid(heatmap, mid_plots, model_data_noSST, nrow = 3 , rel_heights = c(5,4, 5),
labels = c('A', '', 'H'))
setwd('./figures')
saveRDS(full_plot, "figure4-noSST.rds")
pdf(file = "figure4-noSST.pdf",
width = 10, # The width of the plot in inches
height = 12)
print(full_plot)
dev.off()
setwd('../')
print(full_plot)
```