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Final_methylation_DNAm_signatures.Rmd
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Final_methylation_DNAm_signatures.Rmd
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---
title: "R Notebook"
output: html_notebook
---
### import bigwig data ###
```{r}
# Function for importing bigWig data
FeatureBigWig <- function(x) {
coverage <- c()
for (i in 1:length(x)) {
coverage <- c(coverage, list(data.frame(import(x[[i]]))))
}
coverage
}
# Merge the same samples's data
combine <- function(x){
k <- c()
for (i in seq(from = 1, to = length(x), by = 2)) {
k <- c(k, list(data.frame(merge(x[[i]], x[[i + 1]], by.x = c('seqnames', 'start', 'end'), by.y = c('seqnames', 'start', 'end')))))
}
k
}
# Discards bases that have coverage below 10X
filter <- function(cg){
flt <- c()
for (i in 1:length(cg)) {
flt <- c(flt, list(data.frame(cg[[i]][cg[[i]]$score.y <= quantile(cg[[i]]$score.y, .999) & cg[[i]]$score.y >= 10,])))
}
flt
}
# Merge the all samples
final_merge <- function(path) {
library(rtracklayer)
files <- dir(path, full.names = TRUE)
Feature.cell <- FeatureBigWig(files)
merge.cell <- combine(Feature.cell)
filter.merge.cell <- filter(merge.cell)
names.files <- c(names(filter.merge.cell[[1]])[1:3],unique(gsub('.CPG.*', '', dir(path))))
dat = filter.merge.cell[[1]]
for (i in 2:length(filter.merge.cell)) {
dat <- merge(dat, filter.merge.cell[[i]], by.x = c('seqnames', 'start', 'end'), by.y = c('seqnames', 'start', 'end'))
}
a <- dat[,1:3]
for (j in seq(from = 6, to = ncol(dat), by = 6)) {
a <- cbind(a,dat[, j])
}
colnames(a) <- names.files
a
}
```
# Generate signatures codes
```{r}
# Find different methylation positions (DMPs)
DMPs <- function(data, Phenotype, method){
require(limma)
require(dplyr)
message("[===========================]")
message("[<<<< DMPs START >>>>>]")
message("-----------------------------")
design.pairs <- function(levels) {
n <- length(levels)
design <- matrix(0,n,choose(n,2))
rownames(design) <- levels
colnames(design) <- 1:choose(n,2)
k <- 0
for (i in 1:(n - 1))
for (j in (i + 1):n) {
k <- k + 1
design[i,k] <- 1
design[j,k] <- -1
colnames(design)[k] <- paste(levels[i], "-", levels[j],sep = "")
}
design
}
design <- model.matrix(~0 + Phenotype)
contr.matrix <- design.pairs(levels(factor(Phenotype)))
colnames(design) <- rownames(contr.matrix)
Fit <- lmFit(data, design) %>%
contrasts.fit(., contrasts = contr.matrix) %>%
eBayes(.)
if (method == "TM") {
FitList <- list()
for (i in 1:ncol(contr.matrix)) {
FitList[[i]] <- topTable(Fit[which(decideTests(Fit)[,i] != 0),], coef = i, adjust.method = "BH", number = nrow(data)) %>% mutate(ID = rownames(.))
message(paste0(i, " done"))
}
} else if (method == "TIL") {
FitList <- list()
for (i in 1:ncol(contr.matrix)) {
FitList[[i]] <- topTable(Fit, coef = i, adjust.method = "BH", number = nrow(data)) %>% mutate(ID = rownames(.))
message(paste0(i, " done"))
}
}
names(FitList) <- colnames(contr.matrix)
message("[<<<<< DMPs END >>>>>>]")
message("[===========================]")
return(FitList)
}
# Function to get median value of each gene from an input dataframe
getMedVal <- function(data, Phenotype){
library(matrixStats)
Trans <- data.frame(t(data))
Mt.Split <- split(Trans, Phenotype)
Mt.Split <- lapply(Mt.Split, function(x) colMedians(data.matrix(x)))
Mt.Split <- do.call(cbind, Mt.Split)
rownames(Mt.Split) <- rownames(data)
return(Mt.Split)
}
Signfeature <- function(data, Phenotype, FitList, FDR, FCcuoff, MaxDMRs, method) {
require(dplyr)
require(matrixStats)
require(limma)
message("[===========================]")
message("[<<<< Signature START >>>>>]")
message("-----------------------------")
# Filter by thresholds
if (method == "TM") {
A1 <- list()
for (i in 1:length(FitList)) {
A1[[i]] <- filter(FitList[[i]], adj.P.Val < FDR & abs(logFC) > FCcuoff) #%>% arrange(., desc(logFC))
if (nrow(A1[[i]]) > MaxDMRs) {
A1[[i]] <- A1[[i]][1:MaxDMRs,]
}
}
} else if (method == "TIL") {
A1 <- list()
for (i in 1:length(FitList)) {
A1[[i]] <- filter(FitList[[i]], adj.P.Val < FDR & abs(logFC) > FCcuoff) #%>% arrange(., desc(logFC))
if (nrow(A1[[i]]) > MaxDMRs) {
A1[[i]] <- A1[[i]][1:MaxDMRs,]
}
}
}
sig <- lapply(A1, function(x) dplyr::select(x,ID))
sig <- do.call(rbind, sig)
sig <- filter(sig, !duplicated(ID))
data1 <- data[rownames(data) %in% sig$ID,]
# Print number of selected probes (signature)
nrow(data1)
result <- getMedVal(data1, Phenotype)
message("[===========================]")
message("[<<<< Signature END >>>>>]")
message("-----------------------------")
return(result)
}
# function to extract expression values of a list of CpGs
getExpMatrix <- function(geneList, expression){
df_out <- expression[geneList, ]
return(df_out)
}
DGEs.QC <- function(FitList, FDR) {
require(ggpubr)
p <- list()
for (i in 1:length(FitList)) {
p[[i]] <- ggplot(FitList[[i]][FitList[[i]]$adj.P.Val < FDR,], aes(x = logFC)) + geom_density(colour = "red") + labs(title = names(FitList)[i], x = "logFC", y = "Density") + theme(plot.title = element_text(hjust = 0.5)) + scale_x_continuous(breaks = seq(-1, 1, 0.2)) + theme(axis.text.x = element_text(size = 10, color = "Black"), axis.text.y = element_text(size = 10, color = "Black"))
}
return(do.call(ggarrange, p))
}
```
## BPmet signature ##
```{r}
FitList_IC_blood <- DMPs(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[, c(56:80, 93:105)], Phenotype = blueprint_pheno[c(1:25, 38:50)], method = "TIL")
Mediant_IC_blood <- getMedVal(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,c(56:80, 93:105)], Phenotype = blueprint_pheno[-c(26:37,51:52)])
BPmet <- Signfeature(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,c(56:80, 93:105)], Phenotype = blueprint_pheno[-c(26:37,51:52)], FitList = FitList_IC_blood, FDR = 0.05, FCcuoff = 0.2, MaxDMRs = 100, method = "TIL")
```
## BPmetCan signature ##
```{r}
group <- c(rep("normal", 17), rep("cancer", 34), rep("wb", 4))
Pheno2 <- c(as.character(group[18:51]), as.character(blueprint_pheno))
FitList_TM <- DMPs(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,-c(56:107)], Phenotype = group, method = "TM")
# FitList_IC <- DMPs(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,-c(1:55,81:92,106:107)], Phenotype = blueprint_pheno[-c(26:37,51:52)], method = "TIL")
FitList_TIC <- DMPs(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,c(18:51,56:107)], Phenotype = Pheno2, method = "TIL")
Mediant_T <- getMedVal(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,-c(56:107)], Phenotype = group)
# Mediant_IC <- getMedVal(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,-c(1:55,81:92,106:107)], Phenotype = blueprint_pheno[-c(26:37,51:52)])
Mediant_TIC <- getMedVal(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,c(18:51, 56:107)], Phenotype = Pheno2)
Tumor.sig <- Signfeature(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,-c(56:107)], Phenotype = group, FitList = FitList_TM, FDR = 0.05, FCcuoff = 0.5, MaxDMRs = 100, method = "TM")
TIC.sig <- Signfeature(data = Normal_cancer_WB_blueprint_WGBS_genomic_numeric[,c(18:51,56:107)], Phenotype = Pheno2, FitList = FitList_TIC, FDR = 10e-6, FCcuoff = 0.3, MaxDMRs = 300, method = "TIL")
Immune_Tumor_CpGs <- unique(c(rownames(Tumor.sig), rownames(TIC.sig)))
BPmetCan <- getExpMatrix(geneList = Immune_Tumor_CpGs, expression = Mediant_TIC)
```