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RNAseq.Rmd
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RNAseq.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}
#EPI
rm(list = ls())
set.seed(2019)
```
```{r}
library(biomaRt)
library(DESeq2)
library(ggplot2)
library(survminer)
library(survival)
library(msigdbr)
library(fgsea)
library(grid)
library(gridGraphics)
library(limma)
library(ComplexHeatmap)
library(circlize)
library(numbers)
library(ggrepel)
library(ipred)
```
```{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"))
load("all.rdata")
cli$location = ifelse(cli$tumor_location=="supratentorial, temporal lobe", "Temporal", "Other")
```
```{r}
mart <- useMart("ENSEMBL_MART_ENSEMBL", host = "ensembl.org")
ensembl = useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", GRCh = 38)
trl = getBM(attributes=c("ensembl_gene_id","hgnc_symbol", "entrezgene_id", "start_position", "end_position", "chromosome_name", "strand", "gene_biotype"), mart= ensembl) #"description","phenotype_description", "mim_morbid_description",
trl$chromosome_name[nchar(trl$chromosome_name)>3] = gsub("_", "", substr(trl$chromosome_name[nchar(trl$chromosome_name)>3], 10, 11))
```
```{r}
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")))
```
```{r}
x = trl[(((grepl("gene", trl$gene_biotype) & (!(grepl("pseudogene", trl$gene_biotype))))) |((grepl("protein_coding", trl$gene_biotype)))),]
x = x[!x$hgnc_symbol == "",]
```
```{r}
dim(fSeq)
Fhseq = fSeq[order(apply(fSeq, 1, var)[rownames(fSeq)], decreasing = T),]
Fhseq = Fhseq[,order(apply(fSeq, 2, sum)[colnames(fSeq)], decreasing = T)]
dim(Fhseq)
```
```{r}
Fhseq = Fhseq[!(is.na(match(sapply(rownames(Fhseq), function(el){
unlist(strsplit(el, "\\."))[1]
}), x$ensembl_gene_id))),]
dim(Fhseq)
```
```{r}
Fhseq = Fhseq[!(duplicated(x[match(sapply(rownames(Fhseq), function(el){
unlist(strsplit(el, "\\."))[1]
}), x$ensembl_gene_id),"hgnc_symbol"])),]
rownames(Fhseq) = x[match(sapply(rownames(Fhseq), function(el){
unlist(strsplit(el, "\\."))[1]
}), x$ensembl_gene_id), "hgnc_symbol"]
dim(Fhseq)
```
```{r}
Fhseq = Fhseq[,TCGAutils::TCGAsampleSelect(colnames(Fhseq), "01")]
dim(Fhseq)
Fhseq = Fhseq[,!duplicated(substr(colnames(Fhseq), 1,12))]
dim(Fhseq)
```
```{r}
#Differential Expression
```
```{r}
nl = function(x,OL,NL){
x = as.character(x)
if(length(OL)==length(NL)){
for(el in 1:length(OL)){
x[x==(OL[el])]=NL[el]
}
return(x)
} else {
print("ERROR!")
}
}
x = cli[as.character(substr(tolower(colnames(Fhseq)),1,12)),c("age_at_initial_pathologic_diagnosis", "karnofsky_performance_score", "seizure_history", "tumor_location", "histological_type", "neoplasm_histologic_grade","IDH.status","X1p.19q.codeletion","MGMT.promoter.status", "TERT.expression.status")]
x$seizure_history = factor(as.numeric(nl(x$seizure_history,
OL = c("yes", "no"),
NL = c(1, 0))),
labels = c("yes", "no"))
x$histological_type = as.numeric(nl(x$histological_type,
OL = c("oligodendroglioma", "oligoastrocytoma", "astrocytoma", "glioblastoma"),
NL = c(1, 2, 3, 4)))
x$tumor_location = as.numeric(nl(x$tumor_location,
OL = c("posterior fossa, brain stem", "posterior fossa, cerebellum", "supratentorial, frontal lobe", "supratentorial, not otherwise specified", "supratentorial, occipital lobe", "supratentorial, parietal lobe", "supratentorial, temporal lobe"),
NL = c(0,0,0,0,0,0,1)))
x$neoplasm_histologic_grade = as.numeric(nl(x$neoplasm_histologic_grade,
OL = c("g2", "g3", "g4"),
NL = c(2, 3, 4)))
x$IDH.status = factor(as.numeric(nl(x$IDH.status,
OL = c("Mutant", "WT"),
NL = c(1, 0))),
labels = c("Mutant", "WT"))
x$X1p.19q.codeletion = factor(as.numeric(nl(x$X1p.19q.codeletion,
OL = c("codel", "non-codel"),
NL = c(1, 0))),
labels = c("codel", "non-codel"))
x$MGMT.promoter.status = factor(as.numeric(nl(x$MGMT.promoter.status,
OL = c("Methylated", "Unmethylated"),
NL = c(1, 0))),
labels = c("Methylated", "Unmethylated"))
x$TERT.expression.status = factor(as.numeric(nl(x$TERT.expression.status,
OL = c("Expressed", "Not expressed"),
NL = c(1, 0))),
labels = c("Expressed", "Not expressed"))
rownames(x)=colnames(Fhseq)
colnames(x)=c("Age", "Karnofsky", "Seizure_history", "Temporal_location", "Astrocytic_histology", "Grade", "MutIDH", "Cod1p19q", "MetMGMT", "ExprTERT")
```
```{r}
log2(1.05)
```
```{r}
ds = function(M, x, t, n=F, b=T){
f = function(x){
x = as.numeric(x)
#x = 2^x
x[x==-Inf] = min(x[!x==-Inf])-1
x = x - median(x)
x = x / sd(x)
return(x)
}
dds <- DESeqDataSetFromMatrix(countData = M[,!is.na(x[,t])],
colData = x[!is.na(x[,t]),],
design= as.formula(paste("~", t, sep = "")))
dim(dds)
dds <- dds[rowMedians(counts(dds)) >= 5,]
dim(dds)
dds <- DESeq(dds)
dsr <- results(dds)
if(n){
nexp <- assay(vst(dds, blind=b))
an = flip(apply(nexp,1,f))
colnames(an) = rev(colnames(nexp))
y = list(dds,dsr,nexp,an)
names(y) = c("DDS", "DSR", "VST","nexp")
return(y)
} else {
y = list(dds,dsr)
names(y) = c("DDS", "DSR")
return(y)
}
}
```
```{r}
ds_epi = ds(M = Fhseq, x=x, t="Seizure_history", n=T, b=F)
ds_Age = ds(M = Fhseq, x=x, t="Age", n=T, b=T)
for(el in c("Karnofsky", "Temporal_location", "Astrocytic_histology", "Grade", "MutIDH", "Cod1p19q", "MetMGMT", "ExprTERT")){
print(el)
assign (paste("ds", el, sep = "_"), ds(M = Fhseq, x=x, t=el, n=F))
}
```
```{r}
res = as.data.frame(ds_epi$DSR)
res = res[order(res$pvalue),]
```
```{r}
res = cbind(res, trl[match(rownames(res), trl$hgnc_symbol),])
res
```
```{r}
colnames(res) = c("baseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj", "ensembl", "hgnc", "entrez", "start", "end", "chr", "strand", "biotype")
```
```{r}
res$gr="nc"
res[(res$log2FoldChange<log2(1))&((res$padj<(0.1))&(!is.na(res$padj))),"gr"]="Downregulated"
res[(res$log2FoldChange>log2(1))&((res$padj<(0.1))&(!is.na(res$padj))),"gr"]="Upregulated"
dim(res[res$gr=="Upregulated",])
dim(res[res$gr=="Downregulated",])
```
```{r}
for(el in c("Age", "Karnofsky", "Temporal_location", "Astrocytic_histology", "Grade", "MutIDH", "Cod1p19q", "MetMGMT", "ExprTERT")){
res[,paste("pvalue", el, sep="_")] = as.data.frame(get(paste("ds", el, sep="_"))$DSR)[match(rownames(res), rownames(as.data.frame(get(paste("ds", el, sep="_"))$DSR))),]$pvalue
res[,paste("padj", el, sep="_")] = as.data.frame(get(paste("ds", el, sep="_"))$DSR)[match(rownames(res), rownames(as.data.frame(get(paste("ds", el, sep="_"))$DSR))),]$padj
res[,paste("log2FoldChange", el, sep="_")] = as.data.frame(get(paste("ds", el, sep="_"))$DSR)[match(rownames(res), rownames(as.data.frame(get(paste("ds", el, sep="_"))$DSR))),]$log2FoldChange
res[,paste("stat", el, sep="_")] = as.data.frame(get(paste("ds", el, sep="_"))$DSR)[match(rownames(res), rownames(as.data.frame(get(paste("ds", el, sep="_"))$DSR))),]$stat
}
```
```{r}
nexp = ds_epi$nexp
Nexp = ds_Age$nexp
```
```{r}
x = cli[tolower(substr(colnames(Nexp),1,12)),"surv"]
ds_surv = as.data.frame(flip(apply(Nexp, 1, function(X){
summary(coxph(surv~gene, data = data.frame(surv = x, gene = X)))$coefficients[,4:5]
})))
```
```{r}
colnames(ds_surv) = c("p", "Z")
ds_surv$g = ifelse(ds_surv$Z<0, "Positive", "Negative")
ds_surv$padj = p.adjust(ds_surv$p, method = "BH")
ds_surv$g[ds_surv$padj>0.05] = "NS"
```
```{r}
res$pvalue_surv = ds_surv[match(rownames(res), rownames(ds_surv)),]$p
res$padj_surv = ds_surv[match(rownames(res), rownames(ds_surv)),]$padj
res$stat_surv = ds_surv[match(rownames(res), rownames(ds_surv)),]$Z
res$Prognostic = ds_surv[match(rownames(res), rownames(ds_surv)),]$g
```
```{r}
x = ifelse(res$log2FoldChange_MutIDH>0, "Mutant", "WT")
x[res$padj_MutIDH>=0.1] = "NS"
res$IDH = x
x = ifelse(res$log2FoldChange_Karnofsky>0, "Higher", "Lower")
x[res$padj_Karnofsky>=0.1] = "NS"
res$Karnofsky = x
x = ifelse(res$log2FoldChange_MetMGMT>0, "Methylated", "Unmethylated")
x[res$padj_MetMGMT>=0.1] = "NS"
res$MGMT = x
x = ifelse(res$log2FoldChange_Age>0, "Higher", "Lower")
x[res$padj_Age>=0.1] = "NS"
res$Age = x
x = ifelse(res$log2FoldChange_ExprTERT>0, "Expressed", "Not expressed")
x[res$padj_ExprTERT>=0.1] = "NS"
res$TERT = x
x = ifelse(res$log2FoldChange_Cod1p19q>0, "Codel", "Non-codel")
x[res$padj_Cod1p19q>=0.1] = "NS"
res$Cod1p19q = x
x = ifelse(res$log2FoldChange_Astrocytic_histology>0, "Astrocytic", "Oligodendroglial")
x[res$padj_Astrocytic_histology>=0.1] = "NS"
res$Histology = x
x = ifelse(res$log2FoldChange_Grade>0, "Higher", "Lower")
x[res$padj_Grade>=0.1] = "NS"
res$Grade = x
x = ifelse(res$log2FoldChange_Temporal_location>0, "Temporal", "Other")
x[res$padj_Temporal_location>=0.1] = "NS"
res$Location = x
rownames(res) = res$hgnc
```
```{r}
setwd(paste0(wd, "/dt"))
write.csv(res[!res$gr=="nc",], file="DEG.csv")
save(res, file = "res_epi.Rdata")
save(nexp, file = "nexp_epi.Rdata")
save(Nexp, file = "nexp_age.Rdata")
```
```{r}
#Verhaak figures
```
```{r}
c2 = msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CGP")
c2 = c2 %>% split(x = .$gene_symbol, f = .$gs_name)
```
```{r}
setwd(paste0(wd, "/dt"))
load(file = "res_epi.Rdata")
res = res[,!colnames(res)=="Verhaak"]
res$Verhaak = "none"
for(el in intersect(c2$VERHAAK_GLIOBLASTOMA_CLASSICAL, as.character(res$hgnc))){
res[el, "Verhaak"] = "Classical"
}
for(el in intersect(c2$VERHAAK_GLIOBLASTOMA_PRONEURAL, as.character(res$hgnc))){
res[el, "Verhaak"] = "Proneural"
}
for(el in intersect(c2$VERHAAK_GLIOBLASTOMA_MESENCHYMAL, as.character(res$hgnc))){
res[el, "Verhaak"] = "Mesenchymal"
}
for(el in intersect(c2$VERHAAK_GLIOBLASTOMA_NEURAL, as.character(res$hgnc))){
res[el, "Verhaak"] = "Neural"
}
res$Verhaak = factor(res$Verhaak)
for (el in rev(c("Neural", "Proneural", "Classical", "Mesenchymal", "none"))){
res$Verhaak = relevel(res$Verhaak, el)
}
res
```
```{r}
res[!res$Verhaak == "none",]
```
```{r}
fig1V = ggplot(data = res[!(is.na(res$stat)|is.na(res$stat_surv)),], aes(x = stat, y = stat_surv, color = Verhaak)) +
geom_point(alpha = 0) +
geom_point(alpha = 0.1, data = subset(res, Verhaak == 'none')) +
geom_point(alpha = 0.3, data = subset(res, !Verhaak == 'none')) +
xlab("DeSeq2 statistic for seizure history") +
ylab("Z-statistic in survival Cox model\n(DeSeq2 normalized data)") +
labs(title = "Differential expression of genes from TCGA dataset", subtitle = paste0("All genes: Spearman correlation: ρ = ", signif(cor.test(res$stat, res$stat_surv, method = "spearman")$estimate, 3), ", p = ", signif(cor.test(res$stat, res$stat_surv, method = "spearman")$p.value, 3), "\nDEGs: Exact Fisher test: log₂(OR) = ", signif(log2(fisher.test(xtabs(~gr+Prognostic, data = res)[c("Upregulated", "Downregulated"), c("Negative", "Positive")])$estimate), 3), ", p = ", signif(fisher.test(xtabs(~gr+Prognostic, data = res)[c("Upregulated", "Downregulated"), c("Negative", "Positive")])$p.value, 3)), color = "Gene in subtype signature\nby Verhaak et al.") +
#scale_color_discrete(name = "Gene in subtype signature\nby Verhaak et al.", labels = c("Neural", "Proneural", "Classical","Mesenchymal", "none")) +
geom_vline(linetype = "longdash", xintercept = mean(min(res[res$gr == "Upregulated",]$stat), max(res[!res$gr == "Upregulated",]$stat))) +
geom_vline(linetype = "longdash", xintercept = mean(max(res[res$gr == "Downregulated",]$stat), min(res[!res$gr == "Downregulated",]$stat))) +
geom_hline(linetype = "longdash", yintercept = mean(max(res[res$Prognostic == "Positive",]$stat, na.rm = T), min(res[!res$Prognostic == "Positive",]$stat, na.rm = T))) +
geom_hline(linetype = "longdash", yintercept = mean(min(res[res$Prognostic == "Positive",]$stat, na.rm = T), max(res[!res$Prognostic == "Positive",]$stat, na.rm = T))) +
scale_color_manual(values = c("green","orange", "blue", "purple", "gray"))+
theme_classic()
fig1V
```
```{r}
setwd(paste0(wd, "/Figures"))
save(fig1V, file = "fig1V.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(fig1V, filename = "fig1V.png", width = 8, height = 6, dpi = 400)
```
```{r}
Q = setNames(res$log2FoldChange, res$hgnc)
Q = Q[!is.na(res$log2FoldChange)]
Q = Q[!is.na(res$hgnc)]
Q = Q[order(Q, decreasing = T)]
fgseaRes <- fgseaMultilevel(pathways = c2,
stats = Q,
minSize=10,
maxSize=10000)
#
```
```{r}
f = function(el){
pathway = c2[[el]]
stats = Q
gseaParam = 1
rnk <- rank(-stats)
ord <- order(rnk)
statsAdj <- stats[ord]
statsAdj <- sign(statsAdj) * (abs(statsAdj)^gseaParam)
statsAdj <- statsAdj/max(abs(statsAdj))
pathway <- unname(as.vector(na.omit(match(pathway, names(statsAdj)))))
pathway <- sort(pathway)
gseaRes <- calcGseaStat(statsAdj, selectedStats = pathway,
returnAllExtremes = TRUE)
bottoms <- gseaRes$bottoms
tops <- gseaRes$tops
n <- length(statsAdj)
xs <- as.vector(rbind(pathway - 1, pathway))
ys <- as.vector(rbind(bottoms, tops))
toPlot <- data.frame(x = c(0, xs, n + 1), y = c(0, ys, 0))
diff <- (max(tops) - min(bottoms))/8
return(toPlot)
}
df = data.frame(gene = c("", names(Q), ""))
df$x = (1:length(df$gene)) -1
for(EL in c("VERHAAK_GLIOBLASTOMA_NEURAL", "VERHAAK_GLIOBLASTOMA_MESENCHYMAL")){
df[,EL] = ifelse(df$gene %in% c2[[EL]], 1, 0)
df[,paste0(EL, "_ES")] = NA
x = f(EL)
df[match(x$x, df$x), paste0(EL, "_ES")] = x$y
}
df
```
```{r}
Fig1GSEA_Verhaak_Epi = ggplot() +
geom_hline(yintercept = 0) +
geom_line(data = df[!is.na(df$VERHAAK_GLIOBLASTOMA_NEURAL_ES),], aes(x = x, y = VERHAAK_GLIOBLASTOMA_NEURAL_ES, color = "neural"), size = 1.5) +
geom_line(data = df[!is.na(df$VERHAAK_GLIOBLASTOMA_MESENCHYMAL_ES),], aes(x = x, y = VERHAAK_GLIOBLASTOMA_MESENCHYMAL_ES, color = "mesenchymal"), size = 1.5) +
geom_segment(aes(x=df[df$VERHAAK_GLIOBLASTOMA_NEURAL == 1,"x"],xend=df[df$VERHAAK_GLIOBLASTOMA_NEURAL == 1,"x"],y=min(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - 0.05 * (max(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - min(dropNA(unlist(df[,grepl("_ES", colnames(df))])))) ,yend=min(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - 0.15 * (max(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - min(dropNA(unlist(df[,grepl("_ES", colnames(df))])))), color = "neural"), alpha = 0.3) +
geom_segment(aes(x=df[df$VERHAAK_GLIOBLASTOMA_MESENCHYMAL == 1,"x"],xend=df[df$VERHAAK_GLIOBLASTOMA_MESENCHYMAL == 1,"x"],y=min(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - 0.05 * (max(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - min(dropNA(unlist(df[,grepl("_ES", colnames(df))])))) ,yend=min(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - 0.15 * (max(dropNA(unlist(df[,grepl("_ES", colnames(df))]))) - min(dropNA(unlist(df[,grepl("_ES", colnames(df))])))), color = "mesenchymal"), alpha = 0.3) +
scale_x_continuous(expand = c(0, 150)) + scale_y_continuous(expand = c(0, 0.02)) +
scale_color_manual(breaks = c("neural", "mesenchymal"), values = c("green", "purple"), name = "Verhaak et al. glioblastoma subtype signatures in Hs.c2:", labels = c(paste0("Neural: padj = ",signif(as.numeric(fgseaRes[fgseaRes$pathway == "VERHAAK_GLIOBLASTOMA_NEURAL","padj"]),digits = 3), ", NES = ",signif(as.numeric(fgseaRes[fgseaRes$pathway == "VERHAAK_GLIOBLASTOMA_NEURAL","NES"]),digits = 3)), paste0("Mesenchymal: padj = ",signif(as.numeric(fgseaRes[fgseaRes$pathway == "VERHAAK_GLIOBLASTOMA_MESENCHYMAL","padj"]),digits = 3), ", NES = ",signif(as.numeric(fgseaRes[fgseaRes$pathway == "VERHAAK_GLIOBLASTOMA_MESENCHYMAL","NES"]),digits = 3))), guide = guide_legend(direction = "vertical", title.hjust = 0)) +
labs(title = "Gene Set Enrichment Analysis", x = "Genes ranked by seizure history logFC", y = "Enrichment Score") +
theme_classic() + theme(legend.position = "top", legend.justification='left')
```
```{r}
Fig1GSEA_Verhaak_Epi
```
```{r}
setwd(paste0(wd, "/Figures"))
save(Fig1GSEA_Verhaak_Epi, df, file = "Fig1GSEA_Verhaak_Epi.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(Fig1GSEA_Verhaak_Epi, filename = "Fig1GSEA_Verhaak_Epi.png", width = 8, height = 6, dpi = 400)
```
```{r}
#Heatmap
```
```{r}
setwd(paste0(wd, "/dt"))
load(file = "nexp_epi.Rdata")
```
```{r}
library(GSEAlm)
a = matrix(0, nrow = 2, ncol = length(rownames(res[!res$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$gr=="Downregulated"]] = 1
A = GSNormalize(dataset = nexp[colnames(a),], incidence = a)
```
```{r}
x = A["Upregulated",]-A["Downregulated",]
cli$Epi_score = NA
cli[tolower(substr(names(x),1,12)),"Epi_score"] = x
```
```{r}
X = nexp[rownames(res[!res$gr=="nc",][order(res[!res$gr=="nc",]$stat, decreasing = T),]),order(cli[tolower(substr(colnames(nexp), 1,12)), "Epi_score"], decreasing = T)]
x = cli[tolower(substr(colnames(X),1,12)),c("seizure_history", "age_at_initial_pathologic_diagnosis","IDH.status","X1p.19q.codeletion","location", "neoplasm_histologic_grade","histological_type")]
colnames(x) = c("Seizures", "Age", "IDH", "Cod1p19q", "Location", "Grade", "Histology")
col = list(IDH = c("NS" = "gray", "WT" = "cyan", "Mutant" = "darkgoldenrod"),
`Seizures` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Age = colorRamp2(c(min(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T), max(cli[substr(tolower(colnames(X)),1,12),]$age_at_initial_pathologic_diagnosis,na.rm = T)), c("white", "black")),
#TERT = c("NS" = "gray", "Not expressed" = "darkolivegreen4", "Expressed" = "darkorchid4"),
`Seizure history` = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Cod1p19q = c("NS" = "gray", "codel" = "brown", "non-codel" = "cornflowerblue"),
Histology = c("NS" = "gray", "astrocytoma" = "blue", "oligodendroglioma" = "yellow", "oligoastrocytoma" = "green"),
Location = c("NS" = "gray", "Temporal" = "orange", "Other" = "lightblue"),
#Prognostic = c("NS" = "gray", "Positive" = "dodgerblue", "Negative"="deeppink"),
#multiv = c("NS" = "gray", "Positive" = "blue4", "Negative"="red4"),
#verset = c("NS" = "gray", "Positive" = "deepskyblue", "Negative"="red"),
#Karnofsky = colorRamp2(c(min(cli[substr(tolower(colnames(Xn)),1,12),]$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"))
ha <- HeatmapAnnotation(df=x,name = colnames(x),col=col, annotation_name_gp = gpar(fontsize = 9))
ha = Heatmap(matrix = X,
right_annotation = rowAnnotation(foo = anno_mark(at = (1:length(rownames(X)))[rownames(X) %in% c("PCDHGA3", "POSTN")], labels = c("PCDHGA3", "POSTN")), annotation_name_gp = gpar(fontsize = 9)),
top_annotation = ha,
#right_annotation_param = list(labels_gp = gpar(fontsize = 20)),
cluster_columns = F,
cluster_rows = F,
show_row_names = F,
show_column_names = F,
column_title = "Heatmap of differently expressed genes",
name = "Normalized gene expression level",
heatmap_legend_param = list(legend_direction = "horizontal"),
col = colorRamp2(c(min(X), median(X) - 2*sd(X),median(X) - 1*sd(X), median(X), median(X) + 1*sd(X), median(X) + 2*sd(X), max(X)),
c("darkblue", "blue", "cyan", "white", "yellow", "red", "darkred")))
Fig1Heatmap <- grid.grabExpr(draw(ha, heatmap_legend_side = "bottom"))
```
```{r}
setwd(paste0(wd, "/Figures"))
save(Fig1Heatmap, file = "Fig1Heatmap.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(Fig1Heatmap, filename = "Fig1Heatmap.png", width = 8, height = 6, dpi = 400)
```
```{r}
#overlap
```
```{r}
#Y to macierza z ekspresja
#ENS to macierz z nazwami genow (kolumna 1) i nazwami sond (kolumna 2)
#gr1 i gr2 to kolumny zawierajace probki z porownywanych grup
map=function(Y,ENS,gr1,gr2){
Y=Y[apply(Y,1,function(y) (length(gr1)-sum(is.na(y[gr1]))>1) & (length(gr2)-sum(is.na(y[gr2]))>1) ),]
Y=Y[apply(Y,1, function(y) ((sd(y[gr1],na.rm=T)>0) & (sd(y[gr2],na.rm=T)>0)) ),]
ENS=ENS[ENS[,2]!="",]
ENS=ENS[ENS[,2]%in%rownames(Y),]
id_l=tapply(as.character(ENS[,1]),as.character(ENS[,2]),function(z) length(unique(z)))
ENS=ENS[ENS[,2]%in%names(id_l[id_l==1]),]
ens_list=tapply(as.character(ENS[,2]),as.character(ENS[,1]),function(x) list(unique(x)))
ens_list1=unlist(ens_list[sapply(ens_list,length)==1])
V1=Y[ens_list1,]
rownames(V1)=names(ens_list1)
ens_list2=ens_list[!names(ens_list)%in%names(ens_list1)]
{
if (length(ens_list2)>0){
V2=t(sapply(ens_list2,function(x){
AA=Y[x,]
aa=apply(AA,1,function(y) t.test(y[gr1],y[gr2])$p.val)
as.numeric(AA[names(which(aa==min(aa)))[1],])
}))
rownames(V2)=names(ens_list2)
colnames(V2)=colnames(V1)
V=rbind(V1,V2)
}
else
V=V1
}
V=V[sort(rownames(V)),]
V
}
```
```{r}
setwd(paste0(wd, "/dt"))
e=read.table("GSE55918_Matrix_GliomaClusteringAnalysis.txt", sep="\t", header = T)
rownames(e)=as.character(e$Probe.set.ID)
e=e[,!(colnames(e)=="Probe.set.ID")]
es = read.csv("GSE55918.csv")
es=as.data.frame(es)
colnames(es)=c("sample", "file", "source", "organism", "data_set", "array", "download", "study_design", "clustering", "grade", "histology", "age", "surv_month", "censor", "treatment_type", "chemotherapy")
es$nn = paste("sample_", 1:length(es$sample), sep = "")
x = as.character(es$sample)
x[es$data_set=="E-MEXP-567"] = gsub("-", ".", x[es$data_set=="E-MEXP-567"])
x[!is.na(as.numeric(substr(es$sample, 1, 1)))] = paste("X", x[!is.na(as.numeric(substr(es$sample, 1, 1)))], sep = "")
x[es$data_set=="Rich et al."] = gsub("-", ".", x[es$data_set=="Rich et al."])
x[es$data_set=="REMBRANDT"] = gsub("-", ".", x[es$data_set=="REMBRANDT"])
x = gsub("APEEK_p_TCGA_GBM16-Ov15_Expr_HT_HG-U133A_96-HTA_", "APEEK_p_TCGA_GBM16.Ov15_Expr_HT_HG.U133A_96.HTA_", x)
x = gsub("BONES_p_TCGA_Batch8_9_RNA_HT_HG-U133A_96-HTA_", "BONES_p_TCGA_Batch8_9_RNA_HT_HG.U133A_96.HTA_", x)
x = gsub("FEAST_p_TCGA_B20_21_Expression_HT_HG-U133A_96-HTA_", "FEAST_p_TCGA_B20_21_Expression_HT_HG.U133A_96.HTA_", x)
x = gsub("MULEY_p_TCGA_b53and79_Expr_HT_HG-U133A_24-HTA_", "MULEY_p_TCGA_b53and79_Expr_HT_HG.U133A_24.HTA_", x)
x = gsub("NIDUS_p_TCGA_Batch10_RNA_HT_HG-U133A_96-HTA_", "NIDUS_p_TCGA_Batch10_RNA_HT_HG.U133A_96.HTA_", x)
x = gsub("SOCKS_p_TCGA_31_38_RX14_HT_HG-U133A_96-HTA_", "SOCKS_p_TCGA_31_38_RX14_HT_HG.U133A_96.HTA_", x)
x = gsub("SPANS_p_TCGA_Batch60_62_Expr_HT_HG-U133A_24-HTA_", "SPANS_p_TCGA_Batch60_62_Expr_HT_HG.U133A_24.HTA_", x)
x = gsub("TALAR_p_TCGA_Batch62_Expr_HT_HG-U133A_24-HTA_", "TALAR_p_TCGA_Batch62_Expr_HT_HG.U133A_24.HTA_", x)
x = gsub("TARRE_p_MultiPlate_TCGA_SS_MA_Ref_HT_HG-U133A_96-HTA_", "TARRE_p_MultiPlate_TCGA_SS_MA_Ref_HT_HG.U133A_96.HTA_", x)
x = gsub("URGED_p_TCGA_Pop_May2011_HT_HG-U133A_96-HTA_", "URGED_p_TCGA_Pop_May2011_HT_HG.U133A_96.HTA_", x)
x = gsub("WARNS_p_TCGA_b79_Expr_HT_HG-U133A_24-HTA_", "WARNS_p_TCGA_b79_Expr_HT_HG.U133A_24.HTA_", x)
es$on = x
colnames(e) = es$nn[match(colnames(e), es$on)]
es$surv_month[es$surv_month=="Null"]=NA
es$surv_month=as.numeric(as.character(es$surv_month))
es$surv = with(es, Surv(surv_month/12, censor == 1))
es$grade[es$grade=="null"]=NA
es$grade=gsub(" ", "", as.character(es$grade))
annot = data.frame(ID = rownames(e), entrez = gsub("_at", "", rownames(e)))
x = as.character(trl[match(annot$entrez, trl$entrezgene),"hgnc_symbol"])
x[x=="GABRB2"]="GABBR2"
x[x=="GPR98"]="ADGRV1"
annot$hgnc = x
E=map(Y=e, ENS = as.matrix(annot[,c("hgnc", "ID")]), gr1=as.character(es[(es$grade=="G4")&(!is.na(es$grade)),]$nn), gr2=as.character(es[(!es$grade=="G4")&(!is.na(es$grade)),]$nn))
```
```{r}
x = es$surv
E = as.matrix(E[,!is.na(x)])
E = E[rowSums(is.na(E))<500,]
x = x[!is.na(x)]
```
```{r}
ds_verset = as.data.frame(flip(apply(E, 1, function(X){
summary(coxph(surv~gene, data = data.frame(surv = x, gene = X)))$coefficients[,4:5]
})))
```
```{r}
colnames(ds_verset) = c("p", "Z")
ds_verset$g = ifelse(ds_verset$Z<0, "Positive", "Negative")
ds_verset$padj = p.adjust(ds_verset$p, method = "BH")
ds_verset$g[ds_verset$padj>0.05] = "NS"
```
```{r}
res$pvalue_verset = ds_verset[match(rownames(res), rownames(ds_verset)),]$p
res$padj_verset = ds_verset[match(rownames(res), rownames(ds_verset)),]$padj
res$stat_verset = ds_verset[match(rownames(res), rownames(ds_verset)),]$Z
res$verset = ds_verset[match(rownames(res), rownames(ds_verset)),]$g
```
```{r}
sigg = res[!res$gr=="nc",]
```
```{r}
overlap = data.frame(feat = c("Age", "Grade", "Histology", "Prognostic", "TERT", "Cod1p19q", "verset", "IDH", "Location", "Karnofsky", "MGMT"),
name = c("age", "grade", "histology", "prognostic in TCGA", "TERT", "1p19q", "prognostic in GSE", "IDH", "Location", "Karnofsky", "MGMT"))
rownames(overlap)=overlap$feat
nn=NULL
u=NULL
d=NULL
x=NULL
for(el in overlap$feat){
counts=as.data.frame(xtabs(as.formula(paste("~ gr + `", el, "`", sep="")), data = sigg))
table = xtabs(as.formula(paste("Freq ~ gr + `", el, "`", sep="")), data=counts)
table = table[c("Upregulated", "Downregulated"),!colnames(table)=="NS"]
table = table[,order(table[2,],decreasing = F)]
nn = c(nn, paste(colnames(table)[1],overlap[el,"name"]))
FT=fisher.test(table)
u = c(u, table["Upregulated",])
d = c(d, table["Downregulated",])
x = c(x, paste(el, colnames(table), sep = "_"))
overlap[el,"p.epi"]=FT$p.value
overlap[el,"OR.epi"]=FT$estimate
overlap[el,"lCI.epi"]=FT$conf.int[1]
overlap[el,"uCI.epi"]=FT$conf.int[2]
}
overlap$nn= gsub("codel", "Codeleted", nn)
overlap$OR.epi = log2(overlap$OR.epi)
overlap$lCI.epi = log2(overlap$lCI.epi)
overlap$uCI.epi = log2(overlap$uCI.epi)
overlap$padj.epi = p.adjust(overlap$p.epi, method = "BH")
overlap$lab = paste("adj. p = ", signif(overlap$padj.epi, 3), sep = "")
overlap$labpos=-2
overlap$nn = with(overlap, reorder(nn, OR.epi))
fp <- ggplot(data=overlap, aes(x=nn, y=OR.epi, ymin=lCI.epi, ymax=uCI.epi)) +
geom_pointrange() +
geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
geom_text(data=overlap, aes(x = nn, y = labpos, label = lab, hjust=0, colour = "blue")) +
scale_color_manual(values=c("blue")) +
coord_flip() + # flip coordinates (puts labels on y axis)
labs(title="Overlap of associated genes", x=NULL, y = "Upregulated with seizures log2 OR (95% CI)\n") +
theme_classic() + # use a white background
theme(legend.position="none")
Fig1overfp = fp
Fig1overfp
```
```{r}
setwd(paste0(wd, "/Figures"))
save(Fig1overfp, file = "Fig1overfp.Rdata")
ggsave(Fig1overfp, filename = "Fig1overfp.png", width = 8, height = 6, dpi = 400)
```
```{r}
overlap
```
```{r}
X = rep("gray", times = length(x))
names(X)=x
y = list(IDH = c("NS" = "gray", "WT" = "cyan", "Mutant" = "darkgoldenrod"),
Age = c("NS" = "gray", "Lower" = "white", "Higher" = "black"),
TERT = c("NS" = "gray", "Not expressed" = "darkolivegreen4", "Expressed" = "darkorchid4"),
#Seizures = c("NS" = "gray", "yes" = "#008FFF", "no" = "#FF0007"),
Cod1p19q = c("NS" = "gray", "Codel" = "brown", "Non-codel" = "cornflowerblue"),
Histology = c("NS" = "gray", "Astrocytic" = "blue", "Oligodendroglial" = "yellow"),
Grade = c("NS" = "gray", "Lower" = "forestgreen", "Higher" = "pink"),
Location = c("NS" = "gray", "Temporal" = "orange", "Other" = "lightblue"),
Prognostic = c("NS" = "gray", "Positive" = "dodgerblue", "Negative"="deeppink"),
verset = c("NS" = "gray", "Positive" = "deepskyblue", "Negative"="red"),
Karnofsky = c("NS" = "gray", "Higher" = "darkslateblue", "Lower"="lawngreen"),
MGMT = c("NS" = "gray", "Methylated" = "cyan4", "Unmethylated"="coral"))
#multiv = c("NS" = "gray", "Positive" = "blue4", "Negative"="red4"))
for(el in x){
Y = unlist(strsplit(el, "_"))
X[el] = y[[Y[1]]][Y[2]]
}
f = function(x){
return(x[1])
}
df <- data.frame(group = gsub("_", " ", gsub("multiv", "Multivariate prog. in TCGA", gsub("Prognostic", "Prog. in TCGA", gsub("verset", "Prog. in GSE", x)))),
x = rep(1:length(x), 2),
y = c(u, -d),
gr = rep(c("u","d"), each=length(x)),
col = X,
or = overlap[sapply(strsplit(x, "_"),f),"OR.epi"])
df = df[order(df$or, decreasing = T),]
df$X = 2*(rep(1:(length(x)/2), each = 4))+mod(df$x+1,2)
df$group = with(df, reorder(group, X))
df$X = -df$X
df$p = paste(signif(100*(abs(df$y)/(ifelse(df$gr=="u", sum(sigg$gr=="Upregulated"), sum(sigg$gr=="Downregulated")))), 3), "%")
df$labpos = ifelse(df$gr=="u", 180, -340)
p <- ggplot(df, aes(x=X, y=y, fill=group)) +
geom_bar(stat="identity", position="identity", colour = "gray") +
geom_hline(yintercept=c(-sum(sigg$gr=="Downregulated"), 0, sum(sigg$gr=="Upregulated")), lty=c(2,1,2)) +
scale_fill_manual(values=as.character(unique(df$col)))+
geom_text(data=df, aes(x = X, y = labpos, label = p, hjust=0)) +
coord_flip() +
labs(title="Number of overlaping genes", x="Feature", y = "Downregulated Upregulated \n with seizures") +
expand_limits(y = c(-sum(sigg$gr=="Downregulated"), sum(sigg$gr=="Upregulated"))) +
theme_classic() + # use a white background
scale_y_continuous(expand = c(0, 100)) +
theme(legend.position="right", axis.title.y=element_blank(), legend.title=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(), axis.line.y = element_blank(), plot.title = element_text(hjust = 0.5))
Fig1overbar = p
Fig1overbar
```
```{r}
setwd(paste0(wd, "/Figures"))
save(Fig1overbar, file = "Fig1overbar.Rdata")
ggsave(Fig1overbar, filename = "Fig1overbar.png", width = 8, height = 6, dpi = 400)
```
```{r}
setwd(paste0(wd, "/dt"))
load(file = "nexp_age.Rdata")
load(file = "res_epi.Rdata")
```
```{r}
x = c2[(names(c2) %in% c("MUELLER_METHYLATED_IN_GLIOBLASTOMA", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_DN", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_UP", "VERHAAK_GLIOBLASTOMA_CLASSICAL", "VERHAAK_GLIOBLASTOMA_MESENCHYMAL", "VERHAAK_GLIOBLASTOMA_NEURAL", "VERHAAK_GLIOBLASTOMA_PRONEURAL", "ZHENG_GLIOBLASTOMA_PLASTICITY_DN", "ZHENG_GLIOBLASTOMA_PLASTICITY_UP", "BEIER_GLIOMA_STEM_CELL_DN", "BEIER_GLIOMA_STEM_CELL_UP", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_DN", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_UP"))|(grepl("glio", tolower(names(c2))))]
x[["Upregulated"]] = rownames(res[res$gr == "Upregulated",])
x[["Downregulated"]] = rownames(res[res$gr == "Downregulated",])
a = matrix(0, nrow = length(names(x)), ncol = length(intersect(unique(unlist(x)), rownames(Nexp))), dimnames = list(c = names(x), r = intersect(unique(unlist(x)), rownames(Nexp))))
x = lapply(x, intersect, y = colnames(a))
for(el in 1:length(x)){
a[el, x[[el]]] = 1
}
a = a[rowSums(a)>0, colSums(a)>0]
A = GSNormalize(dataset = Nexp[colnames(a),], incidence = a)
```
```{r}
x = as.data.frame(flip(A))
for(el in colnames(x)[grepl("_UP", colnames(x))]){
el = gsub("_UP", "", el)
x[,el] = x[,paste0(el, "_UP")] - mean(x[,paste0(el, "_UP")]) - x[,paste0(el, "_DN")] + mean(x[,paste0(el, "_DN")])
x = x[,!colnames(x)==paste0(el, "_UP")]
x = x[,!colnames(x)==paste0(el, "_DN")]
}
x$Epilepsy = x$Upregulated - x$Downregulated - mean(x$Upregulated) + mean(x$Downregulated)
x = x[,!(colnames(x) %in% c("Upregulated", "Downregulated"))]
colnames(x) = tolower(colnames(x))
colnames(x) = gsub("glioblastoma", "GBM", colnames(x))
colnames(x) = gsub("pdgfb", "PDGFB", colnames(x))
colnames(x) = gsub("gbm", "GBM", colnames(x))
colnames(x) = gsub("ao", "AO", colnames(x))
colnames(x) = gsub("pilocytic_astrocytoma", "PA", colnames(x))
colnames(x) = gsub("_", " ", colnames(x))
'colnames(x) = sapply(colnames(x), function(el){
el = unlist(strsplit(el, " "))
if(length(el)>1){
el = el[2:length(el)]
}
el = paste(el, collapse = " ")
})'
colnames(x) = sapply(colnames(x), function(el){
el = paste0(toupper(substr(el, 1, 1)), substr(el, 2, nchar(el)))
})
signatures = flip(x)
sign_surv = as.data.frame(flip(apply(signatures, 1, function(X){
summary(coxph(surv~gene, data = data.frame(surv = cli[tolower(substr(colnames(signatures),1,12)),"surv"], gene = X)))$coefficients[,4:5]
})))
sign_surv
x = prcomp(x)
X = as.data.frame(x$rotation)
X$name = factor(rownames(X))
X$start = 0
X$surv = sign_surv[rownames(X),]$z
p = ggplot(data = X, aes(x = PC1, y = PC2, label = name)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
labs(title = "Signatures in glioma biology", subtitle = "Principal Component Analysis", x = paste0("PC1, explains ", signif(100*apply(x$x, 2, var)["PC1"] / sum(apply(x$x, 2, var)),3), "% of variability"), y = paste0("PC2, explains ", signif(100*apply(x$x, 2, var)["PC2"] / sum(apply(x$x, 2, var)),3), "% of variability")) +
geom_segment(aes(x = start, y = start, xend = PC1, yend = PC2, color = -surv), size = 1, arrow = arrow(length = unit(0.02, "npc"), type="closed")) +
scale_color_gradient2(name = "Survival\ninfluence", mid = "gray", high = "dodgerblue", low = "deeppink") +
geom_text_repel(data = X[((X$PC1^2 + X$PC2^2)^0.5 > 0.2),], aes(x = PC1, y = PC2, label = name), force = 1, angle = 0, xlim = c(max(X$PC1), NA), hjust = 0, segment.size = 0.5, segment.alpha = 0.5) + #
#geom_text_repel(data = X[((X$PC1^2 + X$PC2^2)^0.5 > 0.2)&X$PC1<0,], fill = "white", min.segment.length = 0.1) + #xlim = c(NA, -0.4),
scale_x_continuous(breaks = 0, limits = c(min(X$PC1), max(X$PC1) + 0.7)) +
scale_y_continuous(breaks = 0) +
coord_fixed() +
theme_classic() +
theme(axis.line.x = element_blank(), axis.line.y = element_blank(), legend.position = "bottom")
ggsave(p, filename = "t1.png", width = 8, height = 6)
Fig1radial = p
```
```{r}
setwd(paste0(wd, "/Figures"))
save(Fig1radial, file = "Fig1radial.Rdata")
setwd(paste0(wd, "/Figures"))
ggsave(Fig1radial, filename = "Fig1radial.png", width = 8, height = 6, dpi = 400)
```
```{r}
x = c2[(names(c2) %in% c("MUELLER_METHYLATED_IN_GLIOBLASTOMA", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_DN", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_UP", "VERHAAK_GLIOBLASTOMA_CLASSICAL", "VERHAAK_GLIOBLASTOMA_MESENCHYMAL", "VERHAAK_GLIOBLASTOMA_NEURAL", "VERHAAK_GLIOBLASTOMA_PRONEURAL", "ZHENG_GLIOBLASTOMA_PLASTICITY_DN", "ZHENG_GLIOBLASTOMA_PLASTICITY_UP", "BEIER_GLIOMA_STEM_CELL_DN", "BEIER_GLIOMA_STEM_CELL_UP", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_DN", "COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_UP"))|(grepl("glio", tolower(names(c2))))]
x[["Upregulated"]] = rownames(res[res$gr == "Upregulated",])
x[["Downregulated"]] = rownames(res[res$gr == "Downregulated",])
a = matrix(0, nrow = length(names(x)), ncol = length(intersect(unique(unlist(x)), rownames(E))), dimnames = list(c = names(x), r = intersect(unique(unlist(x)), rownames(E))))
x = lapply(x, intersect, y = colnames(a))
for(el in 1:length(x)){
a[el, x[[el]]] = 1
}