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GSEA.Rmd
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GSEA.Rmd
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---
title: "R Notebook"
output: html_notebook
---
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}
rm(list = ls())
gc()
```
```{r}
library(ggplot2)
library(survminer)
library(survival)
library(msigdbr)
library(fgsea)
library(limma)
library(ComplexHeatmap)
library(circlize)
library(GSEAlm)
library(ggpubr)
```
```{r}
dropNA = function(x){
x = x[!is.na(x)]
return(x)
}
flip <- function(x){
x = as.matrix(x)
y = t(apply(x, 2, rev))
rownames(y)=colnames(x)
colnames(y)=rev(rownames(x))
return (y)
}
```
```{r}
wd = getwd()
setwd(paste0(wd, "/dt"))
x = ls()
load("all.rdata")
cli$location = ifelse(cli$tumor_location=="supratentorial, temporal lobe", "Temporal", "Other")
cli$localization = ifelse(grepl("posterior fossa", tolower(cli$tumor_location)), "Other", ifelse(cli$supratentorial_localization=="cerebral cortex", "Cortical", ifelse(cli$supratentorial_localization=="not listed in medical record", NA, "Other")))
rm(list = ls()[!(ls() %in% c(x, "cli"))])
```
```{r}
wd = getwd()
setwd(paste0(wd, "/dt"))
load("res_epi.rdata")
load("nexp_epi.Rdata")
load("nexp_age.Rdata")
```
```{r}
#Prerequisites
```
```{r}
fig1s_gsea = list()
```
```{r}
reactome = msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:REACTOME")
reactome = reactome %>% split(x = .$gene_symbol, f = .$gs_name)
names(reactome) = gsub("REACTOME_", "", names(reactome))
names(reactome) = gsub("_", " ", names(reactome))
```
```{r}
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$hgnc)]
Q = Q[!is.na(Q)]
Q = Q[order(Q, decreasing = T)]
reactome_fgsea <- fgseaMultilevel(pathways = reactome,
stats = Q,
minSize=10,
maxSize=10000)
reactome_fgsea[order(reactome_fgsea$NES, decreasing = T),]
reactome_fgsea[order(reactome_fgsea$NES, decreasing = F),]
```
```{r}
p = plotGseaTable(pathways = reactome[c(reactome_fgsea[order(reactome_fgsea$NES, decreasing = T)[1:10],]$pathway, reactome_fgsea[rev(order(reactome_fgsea$NES, decreasing = F)[1:10]),]$pathway)],
stats = Q,
fgseaRes = reactome_fgsea, render = F, gseaParam = 0.5, colwidths = c(6,2,1,0,1.5))
p$grobs[[1]]$label = "Reactome term"
for(X in 1+4*(1:20)){
el = p$grobs[[X]]$label
if(nchar(el)<25){
p$grobs[[X]]$label = el
} else {
x = unlist(lapply(strsplit(el, ''), function(x) which(x == ' ')))[which.min(abs(unlist(lapply(strsplit(el, ''), function(x) which(x == ' '))) - (nchar(el)/2)))]
el = paste(substr(el, 1, x-1), substr(el, x+1, nchar(el)), sep = "\n")
p$grobs[[X]]$label = el
}
print(p$grobs[[X]]$label)
}
fig1s_gsea[["resctome"]] = p
```
```{r}
go = msigdbr(species = "Homo sapiens", category = "C5")
go = go %>% split(x = .$gene_symbol, f = .$gs_name)
names(go) = gsub("GO_", "", names(go))
names(go) = gsub("_", " ", names(go))
```
```{r}
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$hgnc)]
Q = Q[!is.na(Q)]
Q = Q[order(Q, decreasing = T)]
go_fgsea <- fgseaMultilevel(pathways = go,
stats = Q,
minSize=10,
maxSize=10000)
go_fgsea[order(go_fgsea$NES, decreasing = T),]
go_fgsea[order(go_fgsea$NES, decreasing = F),]
```
```{r}
p = plotGseaTable(pathways = go[c(go_fgsea[order(go_fgsea$NES, decreasing = T)[1:10],]$pathway, go_fgsea[rev(order(go_fgsea$NES, decreasing = F)[1:10]),]$pathway)],
stats = Q,
fgseaRes = go_fgsea, render = F, gseaParam = 0.5, colwidths = c(6,2,1,0,1.5))
p$grobs[[1]]$label = "Gene Ontology term"
for(X in 1+4*(1:20)){
el = p$grobs[[X]]$label
if(nchar(el)<25){
p$grobs[[X]]$label = el
} else {
if(nchar(el)>55){
el = paste0(substr(el, 1, 52), "...")
}
x = unlist(lapply(strsplit(el, ''), function(x) which(x == ' ')))[which.min(abs(unlist(lapply(strsplit(el, ''), function(x) which(x == ' '))) - (nchar(el)/2)))]
el = paste(substr(el, 1, x-1), substr(el, x+1, nchar(el)), sep = "\n")
p$grobs[[X]]$label = el
}
print(p$grobs[[X]]$label)
}
fig1s_gsea[["go"]] = p
```
```{r}
c2 = msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CGP")
c2 = c2 %>% split(x = .$gene_symbol, f = .$gs_name)
names(c2) = gsub("_", " ", names(c2))
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$hgnc)]
Q = Q[!is.na(Q)]
Q = Q[order(Q, decreasing = T)]
c2_fgsea <- fgseaMultilevel(pathways = c2,
stats = Q,
minSize=10,
maxSize=10000)
```
```{r}
p = plotGseaTable(pathways = c2[c(c2_fgsea[order(c2_fgsea$NES, decreasing = T)[1:10],]$pathway, c2_fgsea[rev(order(c2_fgsea$NES, decreasing = F)[1:10]),]$pathway)],
stats = Q,
fgseaRes = c2_fgsea, render = F, gseaParam = 0.5, colwidths = c(6,2,1,0,1.5))
p$grobs[[1]]$label = "MsigDB C2 term"
for(X in 1+4*(1:20)){
el = p$grobs[[X]]$label
if(nchar(el)<25){
p$grobs[[X]]$label = el
} else {
if(nchar(el)>55){
el = paste0(substr(el, 1, 52), "...")
}
x = unlist(lapply(strsplit(el, ''), function(x) which(x == ' ')))[which.min(abs(unlist(lapply(strsplit(el, ''), function(x) which(x == ' '))) - (nchar(el)/2)))]
el = paste(substr(el, 1, x-1), substr(el, x+1, nchar(el)), sep = "\n")
p$grobs[[X]]$label = el
}
}
fig1s_gsea[["c2"]] = p
```
```{r}
setwd(paste0(wd, "/Figures/Supplementary"))
ggsave(filename = "fig1s_gsea.png", ggarrange(plotlist = fig1s_gsea, nrow = 1), width = 18, height = 12)
save(fig1s_gsea, file = "fig1s_gsea.Rdata")
```
```{r}
### AEDs start here
```
```{r}
setwd(paste0(wd, "/dt"))
dsigdb = gmtPathways("dsigdb_D1.gmt")
```
```{r}
dsigdb = lapply(dsigdb, function(x){x = intersect(x, res$hgnc)})
dsigdb = dsigdb[sapply(dsigdb, length)>0]
```
```{r}
library(xlsx)
setwd(paste0(wd, "/dt"))
aed = read.xlsx("AED.xlsx", sheetName = "Sheet1")
```
```{r}
names(dsigdb) = tolower(names(dsigdb))
```
```{r}
dsigdb = dsigdb[tolower(names(dsigdb)) %in% tolower(aed$Pharmaceutical)]
```
```{r}
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$hgnc)]
Q = Q[!is.na(Q)]
Q = Q[order(Q, decreasing = T)]
dsigdb_fgsea <- fgseaMultilevel(pathways = dsigdb,
stats = Q,
minSize=4,
maxSize=10000)
dsigdb_fgsea[order(dsigdb_fgsea$NES, decreasing = T),]
dsigdb_fgsea[order(dsigdb_fgsea$NES, decreasing = F),]
```
```{r}
a = matrix(0, nrow = length(names(dsigdb)), ncol = length(intersect(unique(unlist(dsigdb)), rownames(nexp))), dimnames = list(c = names(dsigdb), r = intersect(unique(unlist(dsigdb)), rownames(nexp))))
dsigdb = lapply(dsigdb, intersect, y = colnames(a))
for(el in 1:length(dsigdb)){
a[el, dsigdb[[el]]] = 1
}
a = a[rowSums(a)>0, colSums(a)>0]
a[1:10,1:10]
```
```{r}
library(GSEAlm)
gsealm_dsigdb = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
```
```{r}
x = cli[substr(tolower(colnames(gsealm_dsigdb)),1,12),"seizure_history"]
dsigdb_res = as.data.frame(flip(apply(gsealm_dsigdb, 1, function(X){
#print(cli[substr(tolower(names(X)),1,12),"seizure_history"])
unlist(wilcox.test(gs~epi, data.frame(epi = x, gs = X), conf.int = T)[c("estimate", "p.value")])
})))
dsigdb_res$p.adjusted = p.adjust(dsigdb_res$p.value, method = "BH")
dsigdb_res$effect = (rowMedians(gsealm_dsigdb[,cli[substr(tolower(colnames(gsealm_dsigdb)),1,12),"seizure_history"]=="yes"], na.rm = T) - rowMedians(gsealm_dsigdb[,cli[substr(tolower(colnames(gsealm_dsigdb)),1,12),"seizure_history"]=="no"], na.rm = T))
dsigdb_res = dsigdb_res[order(dsigdb_res$effect, decreasing = T),]
dsigdb_res = cbind(dsigdb_res, dsigdb_fgsea[match(rownames(dsigdb_res), dsigdb_fgsea$pathway),])
dsigdb_res$aed = aed[match(rownames(dsigdb_res), gsub("\\.", "-", tolower(aed$Pharmaceutical))),"Family"]
dsigdb_res$sel = (!duplicated(dsigdb_res$aed)) | (dsigdb_res$p.adjusted<0.05 & (!is.na(dsigdb_res$p.adjusted))) | (dsigdb_res$padj<0.05 & !is.na(dsigdb_res$padj))
dsigdb_res = dsigdb_res[order(pmin(dsigdb_res$p.adjusted, dsigdb_res$padj, na.rm = T)),]
dsigdb_res[dsigdb_res$sel,]
```
```{r}
a = matrix(0, nrow = 2, ncol = length(rownames(res[!res$gr=="nc",])), dimnames = list(r = c("Upregulated", "Downregulated"), c = rownames(res[!res$gr=="nc",])))
a["Upregulated",res$hgnc[res$gr=="Upregulated"]] = 1
a["Downregulated",res$hgnc[res$gr=="Downregulated"]] = 1
a = a[,intersect(rownames(Nexp), colnames(a))]
A = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
x = A["Upregulated",]-A["Downregulated",]
cli$Epi_score = NA
cli[tolower(substr(names(x),1,12)),"Epi_score"] = x
dsigdb_res = as.data.frame(dsigdb_res)
X = gsealm_dsigdb[rownames(dsigdb_res[dsigdb_res$sel,]),]
X = flip(normalizeQuantiles(flip(X)))
X = X[rev(rownames(X)),order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]
```
```{r}
setwd(paste0(wd, "/dt"))
library(xlsx)
drh = read.xlsx("repurposing_drugs_20200324.xlsx", sheetName = "repurposing_drugs_20200324")
drh = drh[!is.na(drh$target),]
```
```{r}
drh = setNames(strsplit(drh$target, "\\|"), drh$pert_iname)
drh = lapply(drh, function(x){x = intersect(x, res$hgnc)})
drh = drh[sapply(drh, length)>0]
names(drh) = tolower(gsub("\\-", " ", names(drh)))
```
```{r}
setwd(paste0(wd, "/dt"))
aed = read.xlsx("AED.xlsx", sheetName = "Sheet1")
```
```{r}
drh = drh[tolower(gsub(" ", "-", names(drh))) %in% tolower(aed$Pharmaceutical)]
```
```{r}
x = unique(unlist(drh))
x = res[x,]
x = x[order(abs(x$stat), decreasing = T),]
x
```
```{r}
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$hgnc)]
Q = Q[!is.na(Q)]
Q = Q[order(Q, decreasing = T)]
drh_fgsea <- fgseaMultilevel(pathways = drh,
stats = Q,
minSize=4,
maxSize=10000)
drh_fgsea[order(drh_fgsea$NES, decreasing = T),]
drh_fgsea[order(drh_fgsea$NES, decreasing = F),]
```
```{r}
a = matrix(0, nrow = length(names(drh)), ncol = length(intersect(unique(unlist(drh)), rownames(nexp))), dimnames = list(c = names(drh), r = intersect(unique(unlist(drh)), rownames(nexp))))
drh = lapply(drh, intersect, y = colnames(a))
for(el in 1:length(drh)){
a[el, drh[[el]]] = 1
}
a = a[rowSums(a)>0, colSums(a)>0]
a[1:10,1:10]
```
```{r}
library(GSEAlm)
gsealm_drh = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
```
```{r}
x = cli[substr(tolower(colnames(gsealm_drh)),1,12),"seizure_history"]
drh_res = as.data.frame(flip(apply(gsealm_drh, 1, function(X){
#print(cli[substr(tolower(names(X)),1,12),"seizure_history"])
unlist(wilcox.test(gs~epi, data.frame(epi = x, gs = X), conf.int = T)[c("estimate", "p.value")])
})))
drh_res$p.adjusted = p.adjust(drh_res$p.value, method = "BH")
drh_res$effect = (rowMedians(gsealm_drh[,cli[substr(tolower(colnames(gsealm_drh)),1,12),"seizure_history"]=="yes"], na.rm = T) - rowMedians(gsealm_drh[,cli[substr(tolower(colnames(gsealm_drh)),1,12),"seizure_history"]=="no"], na.rm = T))
drh_res = drh_res[order(drh_res$effect, decreasing = T),]
drh_res = cbind(drh_res, drh_fgsea[match(rownames(drh_res), drh_fgsea$pathway),])
drh_res$aed = aed[match(rownames(drh_res), gsub("\\.", "-", tolower(aed$Pharmaceutical))),"Family"]
drh_res$sel = (!duplicated(drh_res$aed)) | (drh_res$p.adjusted<0.05 & (!is.na(drh_res$p.adjusted))) | (drh_res$padj<0.05 & !is.na(drh_res$padj))
drh_res["acetazolamide", "sel"] = T
drh_res = drh_res[order(pmin(drh_res$p.adjusted, drh_res$padj, na.rm = T)),]
drh_res[drh_res$sel,]
```
```{r}
a = matrix(0, nrow = 2, ncol = length(rownames(res[!res$gr=="nc",])), dimnames = list(r = c("Upregulated", "Downregulated"), c = rownames(res[!res$gr=="nc",])))
a["Upregulated",res$hgnc[res$gr=="Upregulated"]] = 1
a["Downregulated",res$hgnc[res$gr=="Downregulated"]] = 1
a = a[,intersect(rownames(Nexp), colnames(a))]
A = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
x = A["Upregulated",]-A["Downregulated",]
cli$Epi_score = NA
cli[tolower(substr(names(x),1,12)),"Epi_score"] = x
drh_res = as.data.frame(drh_res)
X = gsealm_drh[rownames(drh_res[drh_res$sel,]),]
X = flip(normalizeQuantiles(flip(X)))
X = X[rev(rownames(X)),order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]
```
```{r}
my_aeds = unique(c("pregabalin", "metharbital", "zonisamide", "felbamate", "clobazam", "topiramate", "acetazolamide", "felbamate", "levetiracetam", "retigabine", "pyridoxal", "carbamazepine", "perampanel", "vigabatrin", "tiagabine", "valproic acid"))
```
```{r}
my_aeds = my_aeds[order(rowMeans(gsealm_drh[my_aeds,(!is.na(cli[substr(tolower(colnames(gsealm_drh)),1,12),"IDH.status"])) & (cli[substr(tolower(colnames(gsealm_drh)),1,12),"IDH.status"] == "Mutant")]) - rowMeans(gsealm_drh[my_aeds,(!is.na(cli[substr(tolower(colnames(gsealm_drh)),1,12),"IDH.status"])) & (cli[substr(tolower(colnames(gsealm_drh)),1,12),"IDH.status"] == "WT")]), decreasing = T)]
setwd(paste0(wd, "/dt"))
save(my_aeds, file = "myaeds.Rdata")
```
```{r}
drh_res$sel = rownames(drh_res) %in% my_aeds
a = matrix(0, nrow = 2, ncol = length(rownames(res[!res$gr=="nc",])), dimnames = list(r = c("Upregulated", "Downregulated"), c = rownames(res[!res$gr=="nc",])))
a["Upregulated",res$hgnc[res$gr=="Upregulated"]] = 1
a["Downregulated",res$hgnc[res$gr=="Downregulated"]] = 1
a = a[,intersect(rownames(Nexp), colnames(a))]
A = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
x = A["Upregulated",]-A["Downregulated",]
cli$Epi_score = NA
cli[tolower(substr(names(x),1,12)),"Epi_score"] = x
X = gsealm_drh[my_aeds,]
X = flip(normalizeQuantiles(flip(X)))
X = X[my_aeds,order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]
#X = X - rowMin(X)
#X = X/(rowMax(X) - rowMin(X))
'X = X[c(drh_fgsea[order(drh_fgsea$NES, decreasing = T)[1:10],]$pathway, drh_fgsea[rev(order(drh_fgsea$NES, decreasing = F)[1:10]),]$pathway),
order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]'
rownames(X) = sapply(rownames(X), function(el){
if(nchar(el)<25){
return(el)
} else {
x = unlist(lapply(strsplit(el, ''), function(x) which(x == ' ')))[which.min(abs(unlist(lapply(strsplit(el, ''), function(x) which(x == ' '))) - (nchar(el)/2)))]
el = paste(substr(el, 1, x-1), substr(el, x+1, nchar(el)), sep = "\n")
return(el)
}
})
x = cli[tolower(substr(colnames(X),1,12)),c("seizure_history", "age_at_initial_pathologic_diagnosis","IDH.status","X1p.19q.codeletion","location", "neoplasm_histologic_grade","histological_type")]
colnames(x) = c("Seizures", "Age", "IDH", "Cod1p19q", "Location", "Grade", "Histology")
col = list(IDH = c("NS" = "gray", "WT" = "cyan", "Mutant" = "darkgoldenrod"),
`Seizures` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Age = colorRamp2(c(min(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T), max(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T)), c("white", "black")),
#TERT = c("NS" = "gray", "Not expressed" = "darkolivegreen4", "Expressed" = "darkorchid4"),
`Seizure history` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Cod1p19q = c("NS" = "gray", "codel" = "brown", "non-codel" = "cornflowerblue"),
Histology = c("NS" = "gray", "astrocytoma" = "blue", "oligodendroglioma" = "yellow", "oligoastrocytoma" = "green", "glioblastoma" = "purple"),
Location = c("NS" = "gray", "Temporal" = "orange", "Other" = "lightblue"),
#Prognostic = c("NS" = "gray", "Positive" = "dodgerblue", "Negative"="deeppink"),
#multiv = c("NS" = "gray", "Positive" = "blue4", "Negative"="red4"),
#verset = c("NS" = "gray", "Positive" = "deepskyblue", "Negative"="red"),
#Karnofsky = colorRamp2(c(min(cli[substr(tolower(colnames(Xn)),1,12),]$karnofsky_performance_score,na.rm = T), max(cli[substr(tolower(colnames(Xn)),1,12),]$karnofsky_performance_score,na.rm = T)), c("lawngreen", "darkslateblue")),
#MGMT = c("NS" = "gray", "Methylated" = "cyan4", "Unmethylated"="coral"),
Grade = c("NS" = "gray", "g2" = "forestgreen", "g3" = "pink", "g4" = "purple"))
x = x[,c("IDH", "Cod1p19q", "Histology", "Grade", "Age", "Location")]
ha = HeatmapAnnotation(IDH = x$IDH, Cod1p19q = x$Cod1p19q, Histology = x$Histology, Grade = x$Grade, Location = x$Location, Age = x$Age, col=col, annotation_name_gp = gpar(fontsize = 9), annotation_legend_param = list(
Age = list(direction = "horizontal"),
IDH = list(nrow = 2),
Cod1p19q = list(nrow = 2),
Histology = list(nrow = 2),
Grade= list(nrow = 2),
Location= list(nrow = 2)))
drh_res$fgsea = -log10(drh_res$padj)
drh_res$fgsea[is.na(drh_res$fgsea)] = 0
drh_res$GSEAlm = -log10(drh_res$p.adjusted)
drh_res$GSEAlm[is.na(drh_res$GSEAlm)] = 0
pvalue_col_fun = colorRamp2(breaks = -log10(c(1, 0.1 +1e-15, 0.1, 0.05 + 1e-15, 0.05, 0.01, 0.001, 1e-4, 1e-15)),
colors = c("white", "white", "yellow", rev((hcl.colors(5, palette = "Viridis"))[c(1,2,3,4,5)]), "black"))
col = list(fgsea = pvalue_col_fun,
GSEAlm = pvalue_col_fun)
ra = rowAnnotation(df = drh_res[rownames(X),c("fgsea", "GSEAlm")], name = c("fgsea", "GSEAlm"), col = col, show_legend = F, annotation_name_side = "top")
ha = Heatmap(matrix = X,
row_order = my_aeds,
top_annotation = ha,
left_annotation = ra,
column_split = factor(gsub("_", "", gsub("NA_", "unknown", paste0(cli[substr(tolower(colnames(X)),1,12),"seizure_history"], "_"))), levels = c("yes", "no", "unknown")),
column_title = c("Positive seizure history", "Negative seizure history", "Unknown seizure history"),
cluster_column_slices = F,
row_names_gp = gpar(fontsize = 11),
cluster_columns = T,
cluster_rows = F,
show_row_names = T,
show_column_names = F,
show_column_dend = F,
clustering_distance_columns = "minkowski",
clustering_method_columns = "ward.D",
#column_title = "Heatmap of differently expressed genes",
name = "GSEAlm-normalized signature",
heatmap_legend_param = list(legend_direction = "horizontal", legend_width = unit(2, "in")),
col = colorRamp2(breaks = c(quantile(X[X<median(X)]), median(X), quantile(X[X>median(X)])),
colors = c(hcl.colors(5, palette = "PuBu"), "white", rev(hcl.colors(5, palette = "YlOrRd")))))
lgd_pvalue = Legend(title = "Adjusted p-values", col_fun = pvalue_col_fun, -log10(c(1, 0.1, 0.05, 0.01, 0.001, 1e-4)), direction = "horizontal", labels = c(1, 0.1, 0.05, 0.01, 0.001, 0.0001), legend_width = unit(2, "in"))
draw(ha, annotation_legend_list = list(lgd_pvalue), merge_legend = T)
```
```{r}
setwd(paste0(wd, "/Figures"))
png(file="fig4_aedhm_drh.png", width = 9, height = 8, units = "in", res = 400)
draw(ha, annotation_legend_list = list(lgd_pvalue), merge_legend = T, heatmap_legend_side = "bottom", annotation_legend_side = "bottom", column_title = "Expression of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"))
dev.off()
```
```{r}
fig4_aedhm_drh <- grid.grabExpr(draw(ha, annotation_legend_list = list(lgd_pvalue), merge_legend = T, heatmap_legend_side = "bottom", annotation_legend_side = "bottom", column_title = "Expression of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"))) #
setwd(paste0(wd, "/Figures"))
save(fig4_aedhm_drh, file = "fig4_aedhm_drh.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedhm_drh, filename = "fig4_aedhm_drh.png", width = 9, height = 8, dpi = 400)
```
```{r}
x = c(,,,)
```
```{r}
library(gtools)
library(ggnewscale)
x = combinations(length(my_aeds), 2, v=1:length(my_aeds), set=TRUE, repeats.allowed=FALSE)
for(el in rev(rownames(drh))){
if(el %in% my_aeds){
my_aeds = c(el, my_aeds)
}
}
my_aeds = my_aeds[!duplicated(my_aeds)]
df = data.frame(x = x[,1], y = x[,2])
rownames(df) = paste(my_aeds[df$x], my_aeds[df$y], sep = "_")
df = cbind(df, as.data.frame(flip(sapply(rownames(df), function(el){
unlist(cor.test(gsealm_drh[my_aeds[df[el,"x"]],],gsealm_drh[my_aeds[df[el,"y"]],])[c("p.value", "estimate")])
}))[rownames(df),]))
df$padj = p.adjust(df$p)
df$p = -log10(df$padj + 1e-200)
p = ggplot() +
geom_point(data = df, mapping = aes(x = x, y = - y, color = estimate.cor, size = sapply(p, function(el){min(el,4)}))) + theme_classic() +
scale_size(name = "adjusted p-value", breaks = c(0, 1, 2, 3, 4), labels = c(1, 0.1, 0.01, 0.001, "\u2264 0.0001"), range = c(3,7), limits = c(0,4)) +
scale_color_gradientn(name = "Correlation\ncoefficient", lim = c(-1, 1), colors = c("blue4", "blue", "cyan", "gray80", "yellow", "red", "red4")) + new_scale_colour() + new_scale("size")
df = data.frame(y = df$x, x = df$y)
rownames(df) = paste(my_aeds[df$x], my_aeds[df$y], sep = "_")
df$com = sapply(rownames(df), function(el){
length(intersect(drh[[my_aeds[df[el,"x"]]]], drh[[my_aeds[df[el,"y"]]]]))
})
df$prop = sapply(rownames(df), function(el){
100*((df[el,"com"]/length(drh[[my_aeds[df[el,"x"]]]])) + (df[el,"com"]/length(drh[[my_aeds[df[el,"y"]]]]))) / 2
})
#df$s = 4*(log10(df$com + 1) - min(log10(df$com + 1)))/(max(log10(df$com + 1))-min(log10(df$com + 1)))
p = p + geom_point(data = df, mapping = aes(x = x, y = -y, size = log10(com+1), colour = prop)) +
scale_colour_gradientn(name = "Proportion of\ncommon targets", lim = c(0, 100), colours = c("white", "green", "forestgreen", "black")) + guides(size = guide_none()) +
scale_size(range = c(0,7)) +
scale_x_discrete(breaks = my_aeds[my_aeds %in% c(df$x, df$y)]) + scale_y_discrete(breaks = rev(my_aeds[my_aeds %in% c(df$y, df$x)])) + scale_x_continuous(breaks = 1:length(my_aeds), labels = my_aeds) + scale_y_continuous(breaks = 0 - (1:length(my_aeds)), labels = my_aeds) +
theme(legend.position = c("right"), legend.direction = "vertical", legend.box = "vertical", panel.border = element_rect(linetype = "solid", fill = NA), panel.grid.major = element_line(colour = "gray"), axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "", title = "Correlation of AEDs signatures", subtitle = "Pearson's correlation")
ggsave(p, filename = "t1.png", width = 10, height = 6, dpi = 400)
```
```{r}
fig4_aedcm_drh <- p
setwd(paste0(wd, "/Figures"))
save(fig4_aedcm_drh, file = "fig4_aedcm_drh.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedcm_drh, filename = "fig4_aedcm_drh.png", width = 10, height = 6, dpi = 400)
```
```{r}
'X = gsealm_drh[rownames(drh_res[drh_res$sel,]),(cli[tolower(substr(colnames(gsealm_drh),1,12)),"seizure_history"]=="yes")&(!is.na(cli[tolower(substr(colnames(gsealm_drh),1,12)),"seizure_history"]))]
x = flip(combn(rownames(X), m =2))
colnames(x) = c("drug1", "drug2")
#x
x = as.data.frame(cbind(x,flip(apply(x, 1,function(el){
setNames(as.numeric(unlist(cor.test(X[el[1],], X[el[2],])[c("p.value", "estimate")])), c("p.value", "estimate"))}))))
x$p.value = as.numeric(as.character(x$p.value))
x$estimate = as.numeric(as.character(x$estimate))
#x$p.value[x$p.value==0] = as.numeric(min(x$p.value[!x$p.value==0]))/(1e300)
x$padj = p.adjust(x$p.value, method = "BH")
X = cor(flip(X), method = "spearman")[rownames(X),rownames(X)]
for(el in 1:(dim(X)[1])){
for(EL in 1:(dim(X)[1])){
if(el == EL){
X[el,EL] = 0
} else{
if(el>EL){
X[rownames(X)[EL],colnames(X)[el]] = -log10(x[(x$drug1 == rownames(X)[el] | x$drug2 == rownames(X)[el]) & (x$drug1 == rownames(X)[EL] | x$drug2 == rownames(X)[EL]),"padj"] + 1e-100) + 3
} else {
X[rownames(X)[EL],colnames(X)[el]] = x[(x$drug1 == rownames(X)[el] | x$drug2 == rownames(X)[el]) & (x$drug1 == rownames(X)[EL] | x$drug2 == rownames(X)[EL]),"estimate"]
}
}
}
}
col_fun = colorRamp2(breaks = c(rev(0-quantile(abs(x$estimate[!x$estimate==0]))), 0, quantile(abs(x$estimate[!x$estimate==0])), -log10(c(1, 0.1 +1e-15, 0.1, 0.05 + 1e-15, 0.05, 0.01, 0.001, 1e-4, 1e-15) + 1e-100) + 3),
colors = c(hcl.colors(5, palette = "PuBu"), "white", rev(hcl.colors(5, palette = "YlOrRd")), c("white", "white", "yellow", rev((hcl.colors(5, palette = "Viridis"))[c(1,2,3,4,5)]), "black")))
ha = Heatmap(X, cluster_rows = F, cluster_columns = F, col = col_fun, show_heatmap_legend = F, row_names_side = "left")
lgd_pvalue = Legend(title = "Adjusted p-values", col_fun = col_fun, at = -log10(c(1, 0.1, 0.05, 0.01, 0.001, 1e-4) + 1e-100) + 3, labels = c(1, 0.1, 0.05, 0.01, 0.001, 0.0001), direction = "horizontal")
lgd_cor = Legend(title = "Correlation coefficient", col_fun = col_fun, at = 0:10/5-1, direction = "horizontal")
draw(ha, annotation_legend_list = list(lgd_pvalue, lgd_cor), merge_legend = T, column_title = "Correlation of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"), heatmap_legend_side = "bottom")'
```
```{r}
'fig4_aedcm_drh <- grid.grabExpr(draw(ha, annotation_legend_list = list(lgd_pvalue, lgd_cor), merge_legend = T, column_title = "Correlation of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"), heatmap_legend_side = "bottom"))
setwd(paste0(wd, "/Figures"))
save(fig4_aedcm_drh, file = "fig4_aedcm_drh.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedcm_drh, filename = "fig4_aedcm_drh.png", width = 6, height = 6, dpi = 400)'
```
```{r}
X = gsealm_drh[my_aeds[my_aeds %in% rownames(drh_res)],(cli[tolower(substr(colnames(gsealm_drh),1,12)),"seizure_history"]=="yes")&(!is.na(cli[tolower(substr(colnames(gsealm_drh),1,12)),"seizure_history"]))]
library(tidyr)
X = (flip(X))
x = setNames(cli[tolower(substr(rownames(X),1,12)),"IDH.status"], rownames(X))
x = apply(X,2,function(el){
wilcox.test(drug~idh, data = data.frame(drug = el, idh = x[names(el)]))$p.value
})
x = p.adjust(x, method = "BH")
x = data.frame(p = x, y = names(x), x = max(X) + 0.25 * (max(X) - min(X)))
x$label = paste0("", signif(x$p, 2))
x$label[x$p < 0.0001] = "< 0.0001"
X = as.data.frame(X)
X$IDH = cli[substr(tolower(rownames(X)),1,12),"IDH.status"]
X = X[!is.na(X$IDH),]
X$IDH = factor(as.character(X$IDH), levels = c("Mutant", "WT"))
X = pivot_longer(X, cols = intersect(colnames(X), names(drh)), names_to = "pharmaceutical")
X$pharmaceutical = factor(as.character(X$pharmaceutical), levels = my_aeds)
library(ggridges)
p = ggplot(data = X) +
geom_density_ridges(aes(x = value, y = pharmaceutical, fill = IDH, point_color = IDH), scale = 0.5, jittered_points = TRUE, position = "raincloud", point_alpha = 0.1) +
scale_fill_manual(name = "IDH status", values = alpha(c("darkgoldenrod", "cyan"), 0.4)) +
scale_discrete_manual(name = "IDH status", aesthetics = "point_color", values = alpha(c("darkgoldenrod", "cyan"), 0.1)) +
geom_boxplot(aes(x = value, y = pharmaceutical), color = "black", fill = "white", width = 0.1, outlier.size = 0) +
geom_text(data = x, aes(x = x, y = y, label = label), angle = 90) +
labs(title = "GSEAlm pharmaceuticals score relation to IDH mutation",
subtitle = paste0("Wilcoxon test adjusted p-value:"),
x = "GSEAlm pharmaceuticals score",
y = "") +
expand_limits(x = max(X$value) + 0.35 * (max(X$value) - min(X$value))) +
theme_classic() + coord_flip() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
ggsave(filename = "t1.png", plot = p, width = 10, height = 5, dpi = 400)
```
```{r}
fig4_aedidh_drh = p
setwd(paste0(wd, "/Figures"))
save(fig4_aedidh_drh, file = "fig4_aedidh_drh.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedidh_drh, filename = "fig4_aedidh_drh.png", width = 10, height = 6, dpi = 400)
```
```{r}
#dsigdb figres
```
```{r}
my_aeds = my_aeds[my_aeds %in% rownames(gsealm_dsigdb)]
```
```{r}
dsigdb_res$sel = rownames(dsigdb_res) %in% my_aeds
a = matrix(0, nrow = 2, ncol = length(rownames(res[!res$gr=="nc",])), dimnames = list(r = c("Upregulated", "Downregulated"), c = rownames(res[!res$gr=="nc",])))
a["Upregulated",res$hgnc[res$gr=="Upregulated"]] = 1
a["Downregulated",res$hgnc[res$gr=="Downregulated"]] = 1
a = a[,intersect(rownames(Nexp), colnames(a))]
A = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
x = A["Upregulated",]-A["Downregulated",]
cli$Epi_score = NA
cli[tolower(substr(names(x),1,12)),"Epi_score"] = x
X = gsealm_dsigdb[my_aeds,]
X = flip(normalizeQuantiles(flip(X)))
X = X[my_aeds,order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]
#X = X - rowMin(X)
#X = X/(rowMax(X) - rowMin(X))
'X = X[c(dsigdb_fgsea[order(dsigdb_fgsea$NES, decreasing = T)[1:10],]$pathway, dsigdb_fgsea[rev(order(dsigdb_fgsea$NES, decreasing = F)[1:10]),]$pathway),
order(cli[tolower(substr(colnames(X), 1,12)), "Epi_score"], decreasing = T)]'
rownames(X) = sapply(rownames(X), function(el){
if(nchar(el)<25){
return(el)
} else {
x = unlist(lapply(strsplit(el, ''), function(x) which(x == ' ')))[which.min(abs(unlist(lapply(strsplit(el, ''), function(x) which(x == ' '))) - (nchar(el)/2)))]
el = paste(substr(el, 1, x-1), substr(el, x+1, nchar(el)), sep = "\n")
return(el)
}
})
x = cli[tolower(substr(colnames(X),1,12)),c("seizure_history", "age_at_initial_pathologic_diagnosis","IDH.status","X1p.19q.codeletion","location", "neoplasm_histologic_grade","histological_type")]
colnames(x) = c("Seizures", "Age", "IDH", "Cod1p19q", "Location", "Grade", "Histology")
col = list(IDH = c("NS" = "gray", "WT" = "cyan", "Mutant" = "darkgoldenrod"),
`Seizures` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Age = colorRamp2(c(min(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T), max(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T)), c("white", "black")),
#TERT = c("NS" = "gray", "Not expressed" = "darkolivegreen4", "Expressed" = "darkorchid4"),
`Seizure history` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Cod1p19q = c("NS" = "gray", "codel" = "brown", "non-codel" = "cornflowerblue"),
Histology = c("NS" = "gray", "astrocytoma" = "blue", "oligodendroglioma" = "yellow", "oligoastrocytoma" = "green", "glioblastoma" = "purple"),
Location = c("NS" = "gray", "Temporal" = "orange", "Other" = "lightblue"),
#Prognostic = c("NS" = "gray", "Positive" = "dodgerblue", "Negative"="deeppink"),
#multiv = c("NS" = "gray", "Positive" = "blue4", "Negative"="red4"),
#verset = c("NS" = "gray", "Positive" = "deepskyblue", "Negative"="red"),
#Karnofsky = colorRamp2(c(min(cli[substr(tolower(colnames(Xn)),1,12),]$karnofsky_performance_score,na.rm = T), max(cli[substr(tolower(colnames(Xn)),1,12),]$karnofsky_performance_score,na.rm = T)), c("lawngreen", "darkslateblue")),
#MGMT = c("NS" = "gray", "Methylated" = "cyan4", "Unmethylated"="coral"),
Grade = c("NS" = "gray", "g2" = "forestgreen", "g3" = "pink", "g4" = "purple"))
x = x[,c("IDH", "Cod1p19q", "Histology", "Grade", "Age", "Location")]
ha = HeatmapAnnotation(IDH = x$IDH, Cod1p19q = x$Cod1p19q, Histology = x$Histology, Grade = x$Grade, Location = x$Location, Age = x$Age, col=col, annotation_name_gp = gpar(fontsize = 9), annotation_legend_param = list(
Age = list(direction = "horizontal"),
IDH = list(nrow = 2),
Cod1p19q = list(nrow = 2),
Histology = list(nrow = 2),
Grade= list(nrow = 2),
Location= list(nrow = 2)))
dsigdb_res$fgsea = -log10(dsigdb_res$padj)
dsigdb_res$fgsea[is.na(dsigdb_res$fgsea)] = 0
dsigdb_res$GSEAlm = -log10(dsigdb_res$p.adjusted)
dsigdb_res$GSEAlm[is.na(dsigdb_res$GSEAlm)] = 0
pvalue_col_fun = colorRamp2(breaks = -log10(c(1, 0.1 +1e-15, 0.1, 0.05 + 1e-15, 0.05, 0.01, 0.001, 1e-4, 1e-15)),
colors = c("white", "white", "yellow", rev((hcl.colors(5, palette = "Viridis"))[c(1,2,3,4,5)]), "black"))
col = list(fgsea = pvalue_col_fun,
GSEAlm = pvalue_col_fun)
ra = rowAnnotation(df = dsigdb_res[rownames(X),c("fgsea", "GSEAlm")], name = c("fgsea", "GSEAlm"), col = col, show_legend = F, annotation_name_side = "top")
ha = Heatmap(matrix = X,
row_order = my_aeds,
top_annotation = ha,
left_annotation = ra,
column_split = factor(gsub("_", "", gsub("NA_", "unknown", paste0(cli[substr(tolower(colnames(X)),1,12),"seizure_history"], "_"))), levels = c("yes", "no", "unknown")),
column_title = c("Positive seizure history", "Negative seizure history", "Unknown seizure history"),
cluster_column_slices = F,
row_names_gp = gpar(fontsize = 11),
cluster_columns = T,
cluster_rows = F,
show_row_names = T,
show_column_names = F,
show_column_dend = F,
clustering_distance_columns = "minkowski",
clustering_method_columns = "ward.D",
#column_title = "Heatmap of differently expressed genes",
name = "GSEAlm-normalized signature",
heatmap_legend_param = list(legend_direction = "horizontal", legend_width = unit(2, "in")),
col = colorRamp2(breaks = c(quantile(X[X<median(X)]), median(X), quantile(X[X>median(X)])),
colors = c(hcl.colors(5, palette = "PuBu"), "white", rev(hcl.colors(5, palette = "YlOrRd")))))
lgd_pvalue = Legend(title = "Adjusted p-values", col_fun = pvalue_col_fun, -log10(c(1, 0.1, 0.05, 0.01, 0.001, 1e-4)), direction = "horizontal", labels = c(1, 0.1, 0.05, 0.01, 0.001, 0.0001), legend_width = unit(2, "in"))
draw(ha, annotation_legend_list = list(lgd_pvalue), merge_legend = T)
```
```{r}
setwd(paste0(wd, "/Figures"))
png(file="fig4_aedhm_dsigdb.png", width = 9, height = 8, units = "in", res = 400)
draw(ha, annotation_legend_list = list(lgd_pvalue), merge_legend = T, heatmap_legend_side = "bottom", annotation_legend_side = "bottom", column_title = "Expression of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"))
dev.off()
```
```{r}
library(gtools)
library(ggnewscale)
for(el in rev(rownames(dsigdb))){
if(el %in% my_aeds){
my_aeds = c(el, my_aeds)
}
}
my_aeds = my_aeds[!duplicated(my_aeds)]
x = combinations(length(my_aeds), 2, v=1:length(my_aeds), set=TRUE, repeats.allowed=FALSE)
df = data.frame(x = x[,1], y = x[,2])
rownames(df) = paste(my_aeds[df$x], my_aeds[df$y], sep = "_")
df = cbind(df, as.data.frame(flip(sapply(rownames(df), function(el){
unlist(cor.test(gsealm_dsigdb[my_aeds[df[el,"x"]],],gsealm_dsigdb[my_aeds[df[el,"y"]],])[c("p.value", "estimate")])
}))[rownames(df),]))
df$padj = p.adjust(df$p)
df$p = -log10(df$padj + 1e-200)
p = ggplot() +
geom_point(data = df, mapping = aes(x = x, y = - y, color = estimate.cor, size = sapply(p, function(el){min(el,4)}))) + theme_classic() +
scale_size(name = "adjusted p-value", breaks = c(0, 1, 2, 3, 4), labels = c(1, 0.1, 0.01, 0.001, "\u2264 0.0001"), range = c(3,7), limits = c(0,4)) +
scale_color_gradientn(name = "Correlation\ncoefficient", lim = c(-1, 1), colors = c("blue4", "blue", "cyan", "gray80", "yellow", "red", "red4")) + new_scale_colour() + new_scale("size")
df = data.frame(y = df$x, x = df$y)
rownames(df) = paste(my_aeds[df$x], my_aeds[df$y], sep = "_")
df$com = sapply(rownames(df), function(el){
length(intersect(dsigdb[[my_aeds[df[el,"x"]]]], dsigdb[[my_aeds[df[el,"y"]]]]))
})
df$prop = sapply(rownames(df), function(el){
100*((df[el,"com"]/length(dsigdb[[my_aeds[df[el,"x"]]]])) + (df[el,"com"]/length(dsigdb[[my_aeds[df[el,"y"]]]]))) / 2
})
#df$s = 4*(log10(df$com + 1) - min(log10(df$com + 1)))/(max(log10(df$com + 1))-min(log10(df$com + 1)))
p = p + geom_point(data = df, mapping = aes(x = x, y = -y, size = log10(com+1), colour = prop)) +
scale_colour_gradientn(name = "Proportion of\ncommon targets", lim = c(0, 100), colours = c("white", "green", "forestgreen", "black")) + guides(size = guide_none()) +
scale_size(range = c(0,7)) +
scale_x_discrete(breaks = my_aeds[my_aeds %in% c(df$x, df$y)]) + scale_y_discrete(breaks = rev(my_aeds[my_aeds %in% c(df$y, df$x)])) + scale_x_continuous(breaks = 1:length(my_aeds), labels = my_aeds) + scale_y_continuous(breaks = 0 - (1:length(my_aeds)), labels = my_aeds) +
theme(legend.position = c("right"), legend.direction = "vertical", legend.box = "vertical", panel.border = element_rect(linetype = "solid", fill = NA), panel.grid.major = element_line(colour = "gray"), axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "", title = "Correlation of AEDs signatures", subtitle = "Pearson's correlation")
ggsave(p, filename = "t1.png", width = 10, height = 6, dpi = 400)
```
```{r}
fig4_aedcm_dsigdb <- p
setwd(paste0(wd, "/Figures"))
save(fig4_aedcm_dsigdb, file = "fig4_aedcm_dsigdb.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedcm_dsigdb, filename = "fig4_aedcm_dsigdb.png", width = 10, height = 6, dpi = 400)
```
```{r}
'X = gsealm_dsigdb[rownames(dsigdb_res[dsigdb_res$sel,]),(cli[tolower(substr(colnames(gsealm_dsigdb),1,12)),"seizure_history"]=="yes")&(!is.na(cli[tolower(substr(colnames(gsealm_dsigdb),1,12)),"seizure_history"]))]
x = flip(combn(rownames(X), m =2))
colnames(x) = c("drug1", "drug2")
#x
x = as.data.frame(cbind(x,flip(apply(x, 1,function(el){
setNames(as.numeric(unlist(cor.test(X[el[1],], X[el[2],])[c("p.value", "estimate")])), c("p.value", "estimate"))}))))
x$p.value = as.numeric(as.character(x$p.value))
x$estimate = as.numeric(as.character(x$estimate))
#x$p.value[x$p.value==0] = as.numeric(min(x$p.value[!x$p.value==0]))/(1e300)
x$padj = p.adjust(x$p.value, method = "BH")
X = cor(flip(X), method = "spearman")[rownames(X),rownames(X)]
for(el in 1:(dim(X)[1])){
for(EL in 1:(dim(X)[1])){
if(el == EL){
X[el,EL] = 0
} else{
if(el>EL){
X[rownames(X)[EL],colnames(X)[el]] = -log10(x[(x$drug1 == rownames(X)[el] | x$drug2 == rownames(X)[el]) & (x$drug1 == rownames(X)[EL] | x$drug2 == rownames(X)[EL]),"padj"] + 1e-100) + 3
} else {
X[rownames(X)[EL],colnames(X)[el]] = x[(x$drug1 == rownames(X)[el] | x$drug2 == rownames(X)[el]) & (x$drug1 == rownames(X)[EL] | x$drug2 == rownames(X)[EL]),"estimate"]
}
}
}
}
col_fun = colorRamp2(breaks = c(rev(0-quantile(abs(x$estimate[!x$estimate==0]))), 0, quantile(abs(x$estimate[!x$estimate==0])), -log10(c(1, 0.1 +1e-15, 0.1, 0.05 + 1e-15, 0.05, 0.01, 0.001, 1e-4, 1e-15) + 1e-100) + 3),
colors = c(hcl.colors(5, palette = "PuBu"), "white", rev(hcl.colors(5, palette = "YlOrRd")), c("white", "white", "yellow", rev((hcl.colors(5, palette = "Viridis"))[c(1,2,3,4,5)]), "black")))
ha = Heatmap(X, cluster_rows = F, cluster_columns = F, col = col_fun, show_heatmap_legend = F, row_names_side = "left")
lgd_pvalue = Legend(title = "Adjusted p-values", col_fun = col_fun, at = -log10(c(1, 0.1, 0.05, 0.01, 0.001, 1e-4) + 1e-100) + 3, labels = c(1, 0.1, 0.05, 0.01, 0.001, 0.0001), direction = "horizontal")
lgd_cor = Legend(title = "Correlation coefficient", col_fun = col_fun, at = 0:10/5-1, direction = "horizontal")
draw(ha, annotation_legend_list = list(lgd_pvalue, lgd_cor), merge_legend = T, column_title = "Correlation of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"), heatmap_legend_side = "bottom")'
```
```{r}
'fig4_aedcm_dsigdb <- grid.grabExpr(draw(ha, annotation_legend_list = list(lgd_pvalue, lgd_cor), merge_legend = T, column_title = "Correlation of antiepileptic medications genetic signatures", column_title_gp = gpar(fontface = "bold"), heatmap_legend_side = "bottom"))
setwd(paste0(wd, "/Figures"))
save(fig4_aedcm_dsigdb, file = "fig4_aedcm_dsigdb.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig4_aedcm_dsigdb, filename = "fig4_aedcm_dsigdb.png", width = 6, height = 6, dpi = 400)'