In liumoLM/SigQBiC: Signature-QBiC:Predict the effect of mutational signatures on TF binding
It is difficult to provide everything for the whole pipeline, as some data is downloaded from QBiC website and some data is stored locally. The code is here for reference.
Please feel free to email me if you are interested in any part of this study and I would like to provide more details about processing data.
required packages
library(gplots)
library(RColorBrewer)
library(data.table)
library(enrichR)
library(ggplot2)
library(devtools)
library(stringi)
library('lsa') # for cosine()
unartifact.signatures <- c("SBS1", "SBS2" , "SBS3" , "SBS4" , "SBS5" ,"SBS6", "SBS7a" , "SBS7b", "SBS7c", "SBS7d" , "SBS8", "SBS9","SBS10a" ,"SBS10b","SBS11" , "SBS12" , "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21" , "SBS22" , "SBS23" , "SBS24", "SBS25", "SBS26", "SBS28", "SBS29", "SBS30" ,"SBS31" , "SBS32", "SBS33" , "SBS34" , "SBS35", "SBS36" , "SBS37" , "SBS38" , "SBS39" ,"SBS40" , "SBS41",
"SBS42" , "SBS44" )
Overview of TF
I used Signature-QBiC to generate GRs and LRs for all PBMs with all unartifact signatures. Format is PBM - GR - LR.
#TF.uPBM.info is read from TableS4
TF.median.GRs <- data.frame(TF.uPBM.info$Gene)
TF.median.LRs <- data.frame(TF.uPBM.info$Gene)
for(i in 1:nrow(TF.uPBM.info)){
TF.uPBMs <- unlist(strsplit(TF.uPBM.info$PBMs[i],";"))
for(signature in unartifact.signatures){
output.name.genome <- paste("PCAWG.",signature,".genome.mean.ratio.matrix",sep="")
matrix.genome <- get(output.name.genome)
TF.median.GRs[i,signature] <- median(matrix.genome$LR[which(matrix.genome$uPBM %in% TF.uPBMs)])
TF.median.LRs[i,signature] <- median( matrix.genome$GR[which(matrix.genome$uPBM %in% TF.uPBMs)])
}
}
all.TF.LRs <- as.matrix(TF.median.LRs[,-1]) ##LRs, the first column is TF name
all.TF.GRs <- as.matrix(TF.median.GRs[,-1]) ##GRs
row.names(all.TF.LRs) <- TF.uPBM.info$Gene ##data1 and data2 can be found in
row.names(all.TF.GRs) <- TF.uPBM.info$Gene
data <- t(as.matrix(rbind(-all.TF.LRs,all.TF.GRs)))
col = c("#081D58", "#253494","#225EA8","#1D91C0","#41B6C4","#A1DAB4","#FFFFCC","#FFFFCC","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#800026")
##heatmap in Figure 3
hm <- heatmap.2(data,trace="none",col = c("#081D58", "#253494","#225EA8","#1D91C0","#41B6C4","#FFFFCC","#FFFFCC","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#800026"),
hclustfun = function(x) hclust(x,method = "complete"),
hline =TRUE,vline=TRUE,symkey=F,breaks=c(-3.5,-3,-2.5,-2,-1.5,-1,0,1,1.5,2,2.5,3,3.5),
dendrogram = "both",
labCol = NA,
tracecol = "red",
lhei=c(0.5,4), lwid=c(0.2,3.5), keysize=0.75, key.par = list(cex=0.5))
TF.gain.loss.binding.number <- data.frame(unartifact.signatures) #count number of TFs with GR or LR >1
for(signature in unartifact.signatures){
TF.gain.loss.binding.number$gain.TF.numbers[which(TF.gain.loss.binding.number[,1]==signature)] <- length(unique(TF.median.GRs$Gene[TF.median.GRs[,signature]>1]))
TF.gain.loss.binding.number$loss.TF.numbers[which(TF.gain.loss.binding.number[,1]==signature)] <- length(unique(TF.median.LRs$Gene[TF.median.LRs[,signature]>1]))
}
plot.signature.TF.summary <- data.frame(c(TF.gain.loss.binding.number$gain.TF.numbers,
TF.gain.loss.binding.number$loss.TF.numbers))
plot.signature.TF.summary$signature <- c(unartifact.signatures,unartifact.signatures)
plot.signature.TF.summary$category <- rep(c("TFs gaining binding affinity", "TFs abrogating binding affinity"),each=47)
colnames(plot.signature.TF.summary)[1] <- "TF.numbers"
plot.signature.TF.summary.gain <- plot.signature.TF.summary[1:47,]
plot.signature.TF.summary.gain <- plot.signature.TF.summary.gain[order(plot.signature.TF.summary.gain$TF.numbers,decreasing=T),]
##barplot in Figure 3
ggplot(data=plot.signature.TF.summary.gain, aes(x=signature, y=TF.numbers, fill=category)) +
geom_bar(stat="identity")+
scale_fill_manual("legend", values = c("TFs gaining binding affinity" = "#FC4E2A", "TFs abrogating binding affinity" = "#1D91C0"))+
theme_bw()+theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1)) +scale_x_discrete(limits=plot.signature.TF.summary.gain$signature)+ theme(legend.position = "none")
plot.signature.TF.summary.loss <- plot.signature.TF.summary[48:94,]
plot.signature.TF.summary.loss <- plot.signature.TF.summary.loss[order(plot.signature.TF.summary.loss$TF.numbers,decreasing=T),]
ggplot(data=plot.signature.TF.summary.loss, aes(x=signature, y=TF.numbers, fill=category)) +
geom_bar(stat="identity")+
scale_fill_manual("legend", values = c("TFs gaining binding affinity" = "#FC4E2A", "TFs abrogating binding affinity" = "#1D91C0"))+
theme_bw()+theme(text = element_text(size=20),
axis.text.x = element_text(angle=90, hjust=1)) +scale_x_discrete(limits=plot.signature.TF.summary.loss$signature)+ theme(legend.position = "none")
dev.off()
TF.DBD in each cluster Figure 3
hc.cutree.for.TF.clustering <- cutree(as.hclust(hm$colDendrogram), 1:1164)
cluster.A.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==2,4])
cluster.B.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==1,4])
cluster.C.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==4,4])
cluster.D.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==3,4])
cluster.A.TF <- data.frame(cluster.A.TF)
cluster.A.TF$cluster <- "A"
cluster.A.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.A.TF[,1],TF.uPBM.info$Gene)]
cluster.B.TF <- data.frame(cluster.B.TF)
cluster.B.TF$cluster <- "B"
cluster.B.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.B.TF[,1],TF.uPBM.info$Gene)]
cluster.C.TF <- data.frame(cluster.C.TF)
cluster.C.TF$cluster <- "C"
cluster.C.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.C.TF[,1],TF.uPBM.info$Gene)]
cluster.D.TF <- data.frame(cluster.D.TF)
cluster.D.TF$cluster <- "D"
cluster.D.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.D.TF[,1],TF.uPBM.info$Gene)]
##organize all clustering information together
cluster.composition.summary <- data.frame(matrix(nrow=0,ncol=4))
for(cluster.name in c("cluster.A.TF","cluster.B.TF","cluster.C.TF",
"cluster.D.TF")){
cluster <- get(cluster.name)
colnames(cluster)[1] <- "TFname"
cluster <- cluster[!duplicated(cluster$TFname),]
cluster.composition.matrix <- data.frame(table(cluster$TF.family))
cluster.composition.matrix$cluster <- cluster.name
cluster.composition.matrix$percentage <- cluster.composition.matrix$Freq/sum(cluster.composition.matrix$Freq)
cluster.composition.summary <- rbind(cluster.composition.summary,cluster.composition.matrix)
}
dbs <- listEnrichrDbs()
dbs.selected <- c("Reactome_2016")
for(signature in unartifact.signatures){
gain.TF.list <- TF.median.GRs$Gene[TF.median.GRs[,signature]>1] ##selected TFs with GR>1
PathwaySelection(gain.TF.list,0.005,"Reactome_2016")
loss.TF.list <- TF.median.LRs$Gene[TF.median.LRs[,signature]>1] ##selected TFs with LR>1
PathwaySelection(loss.TF.list,0.005,"Reactome_2016")
}
pathways.summary.gain.binding <- data.frame(matrix(nrow=0,ncol=13))
pathways.summary.loss.binding <- data.frame(matrix(nrow=0,ncol=13))
for(signature in unartifact.signatures){
outname.enrichr.gain <- paste("filtered.median.enrichR.Reactome.gained.by.",signature,sep="")
outname.enrichr.loss <- paste("filtered.median.enrichR.Reactome.lossed.by.",signature,sep="")
gain.matrix <- get(outname.enrichr.gain)
loss.matrix <- get(outname.enrichr.loss)
if(nrow(gain.matrix)>0){
gain.matrix$Signature <- signature
colnames(pathways.summary.gain.binding) <- colnames(gain.matrix)
pathways.summary.gain.binding <- rbind(pathways.summary.gain.binding,gain.matrix)
}
if(nrow(loss.matrix)>0){
loss.matrix$Signature <- signature
colnames(pathways.summary.loss.binding) <- colnames(loss.matrix)
pathways.summary.loss.binding <- rbind(pathways.summary.loss.binding,loss.matrix)
}
}
combined.pathways <- unique(c(pathways.summary.gain.binding$New.Term,pathways.summary.loss.binding$New.Term))
combined.dot.matrix.plot <- data.frame(matrix(nrow=length(combined.pathways),ncol=47))
row.names(combined.dot.matrix.plot) <- combined.pathways
colnames(combined.dot.matrix.plot) <- unartifact.signatures
combined.dot.matrix.plot[,] <- 0
for(i in 1:nrow(combined.dot.matrix.plot)){
if(length(which(pathways.summary.gain.binding$New.Term==
row.names(combined.dot.matrix.plot)[i]))>0){
signature.list <- pathways.summary.gain.binding$Signature[which(pathways.summary.gain.binding$New.Term==
row.names(combined.dot.matrix.plot)[i])]
combined.dot.matrix.plot[i,signature.list] <- combined.dot.matrix.plot[i,signature.list] + 1
}
if(length(which(pathways.summary.loss.binding$New.Term==
row.names(combined.dot.matrix.plot)[i]))>0){
signature.list <- pathways.summary.loss.binding$Signature[which(pathways.summary.loss.binding$New.Term==
row.names(combined.dot.matrix.plot)[i])]
combined.dot.matrix.plot[i,signature.list] <- combined.dot.matrix.plot[i,signature.list] + 2
}
}
summary.matrix <- data.frame(combined.pathways)
for(i in 1:nrow(summary.matrix)){
summary.matrix$freq[i] <- sum(combined.dot.matrix.plot[i,]!=0)
}
specific.pathway.order <- summary.matrix$combined.pathways[order(summary.matrix$freq,decreasing = T)]
plot.dot.matrix <- melt(as.matrix(combined.dot.matrix.plot))
colnames(plot.dot.matrix) <- c("Pathway","Signature","value")
mycol <- c("grey","tomato1", "dodgerblue", "darkorchid3")
sum.of.pathways.both <- data.frame(unartifact.signatures)
sum.of.pathways.gain <- data.frame(unartifact.signatures)
sum.of.pathways.loss <- data.frame(unartifact.signatures)
for(i in 1:length(unartifact.signatures)){
sum.of.pathways.both$freq[i] <- sum(combined.dot.matrix.plot[,i]==3)
sum.of.pathways.loss$freq[i] <- sum(combined.dot.matrix.plot[,i]==2)
sum.of.pathways.gain$freq[i] <- sum(combined.dot.matrix.plot[,i]==1)
}
for(i in 1:nrow(sum.of.pathways.both)){
signature <- sum.of.pathways.both$Signature[i]
selected.matrix <- PCAWG_sigProfiler_SBS_signatures_in_samples[which(PCAWG_sigProfiler_SBS_signatures_in_samples[,signature]!=0),]
sum.of.pathways.both$type.of.cancer[i] <- length(unique(selected.matrix$Cancer.Types))
}
sum.of.pathways.both$Category <- "Both"
sum.of.pathways.loss$Category <- "Loss"
sum.of.pathways.gain$Category <- "Gain"
colnames(sum.of.pathways.both) <- c("Signature","Freq","Category")
colnames(sum.of.pathways.loss) <- c("Signature","Freq","Category")
colnames(sum.of.pathways.gain) <- c("Signature","Freq","Category")
sum.of.pathways <- rbind(sum.of.pathways.both,sum.of.pathways.loss,sum.of.pathways.gain)
for(i in 1:nrow(sum.of.pathways)){
sum.of.pathways$Sum[i] <- sum(sum.of.pathways$Freq[which(sum.of.pathways$Signature==sum.of.pathways$Signature[i])])
}
specific.signature.order <- unique(sum.of.pathways$Signature[order(sum.of.pathways$Sum,decreasing=T)])
liumoLM/SigQBiC documentation built on Feb. 5, 2021, 12:53 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
It is difficult to provide everything for the whole pipeline, as some data is downloaded from QBiC website and some data is stored locally. The code is here for reference. Please feel free to email me if you are interested in any part of this study and I would like to provide more details about processing data.
required packages
library(gplots) library(RColorBrewer) library(data.table) library(enrichR) library(ggplot2) library(devtools) library(stringi) library('lsa') # for cosine()
unartifact.signatures <- c("SBS1", "SBS2" , "SBS3" , "SBS4" , "SBS5" ,"SBS6", "SBS7a" , "SBS7b", "SBS7c", "SBS7d" , "SBS8", "SBS9","SBS10a" ,"SBS10b","SBS11" , "SBS12" , "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21" , "SBS22" , "SBS23" , "SBS24", "SBS25", "SBS26", "SBS28", "SBS29", "SBS30" ,"SBS31" , "SBS32", "SBS33" , "SBS34" , "SBS35", "SBS36" , "SBS37" , "SBS38" , "SBS39" ,"SBS40" , "SBS41", "SBS42" , "SBS44" )
Overview of TF
I used Signature-QBiC to generate GRs and LRs for all PBMs with all unartifact signatures. Format is PBM - GR - LR.
#TF.uPBM.info is read from TableS4 TF.median.GRs <- data.frame(TF.uPBM.info$Gene) TF.median.LRs <- data.frame(TF.uPBM.info$Gene) for(i in 1:nrow(TF.uPBM.info)){ TF.uPBMs <- unlist(strsplit(TF.uPBM.info$PBMs[i],";")) for(signature in unartifact.signatures){ output.name.genome <- paste("PCAWG.",signature,".genome.mean.ratio.matrix",sep="") matrix.genome <- get(output.name.genome) TF.median.GRs[i,signature] <- median(matrix.genome$LR[which(matrix.genome$uPBM %in% TF.uPBMs)]) TF.median.LRs[i,signature] <- median( matrix.genome$GR[which(matrix.genome$uPBM %in% TF.uPBMs)]) } } all.TF.LRs <- as.matrix(TF.median.LRs[,-1]) ##LRs, the first column is TF name all.TF.GRs <- as.matrix(TF.median.GRs[,-1]) ##GRs row.names(all.TF.LRs) <- TF.uPBM.info$Gene ##data1 and data2 can be found in row.names(all.TF.GRs) <- TF.uPBM.info$Gene data <- t(as.matrix(rbind(-all.TF.LRs,all.TF.GRs))) col = c("#081D58", "#253494","#225EA8","#1D91C0","#41B6C4","#A1DAB4","#FFFFCC","#FFFFCC","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#800026") ##heatmap in Figure 3 hm <- heatmap.2(data,trace="none",col = c("#081D58", "#253494","#225EA8","#1D91C0","#41B6C4","#FFFFCC","#FFFFCC","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#800026"), hclustfun = function(x) hclust(x,method = "complete"), hline =TRUE,vline=TRUE,symkey=F,breaks=c(-3.5,-3,-2.5,-2,-1.5,-1,0,1,1.5,2,2.5,3,3.5), dendrogram = "both", labCol = NA, tracecol = "red", lhei=c(0.5,4), lwid=c(0.2,3.5), keysize=0.75, key.par = list(cex=0.5)) TF.gain.loss.binding.number <- data.frame(unartifact.signatures) #count number of TFs with GR or LR >1 for(signature in unartifact.signatures){ TF.gain.loss.binding.number$gain.TF.numbers[which(TF.gain.loss.binding.number[,1]==signature)] <- length(unique(TF.median.GRs$Gene[TF.median.GRs[,signature]>1])) TF.gain.loss.binding.number$loss.TF.numbers[which(TF.gain.loss.binding.number[,1]==signature)] <- length(unique(TF.median.LRs$Gene[TF.median.LRs[,signature]>1])) } plot.signature.TF.summary <- data.frame(c(TF.gain.loss.binding.number$gain.TF.numbers, TF.gain.loss.binding.number$loss.TF.numbers)) plot.signature.TF.summary$signature <- c(unartifact.signatures,unartifact.signatures) plot.signature.TF.summary$category <- rep(c("TFs gaining binding affinity", "TFs abrogating binding affinity"),each=47) colnames(plot.signature.TF.summary)[1] <- "TF.numbers" plot.signature.TF.summary.gain <- plot.signature.TF.summary[1:47,] plot.signature.TF.summary.gain <- plot.signature.TF.summary.gain[order(plot.signature.TF.summary.gain$TF.numbers,decreasing=T),] ##barplot in Figure 3 ggplot(data=plot.signature.TF.summary.gain, aes(x=signature, y=TF.numbers, fill=category)) + geom_bar(stat="identity")+ scale_fill_manual("legend", values = c("TFs gaining binding affinity" = "#FC4E2A", "TFs abrogating binding affinity" = "#1D91C0"))+ theme_bw()+theme(text = element_text(size=20), axis.text.x = element_text(angle=90, hjust=1)) +scale_x_discrete(limits=plot.signature.TF.summary.gain$signature)+ theme(legend.position = "none") plot.signature.TF.summary.loss <- plot.signature.TF.summary[48:94,] plot.signature.TF.summary.loss <- plot.signature.TF.summary.loss[order(plot.signature.TF.summary.loss$TF.numbers,decreasing=T),] ggplot(data=plot.signature.TF.summary.loss, aes(x=signature, y=TF.numbers, fill=category)) + geom_bar(stat="identity")+ scale_fill_manual("legend", values = c("TFs gaining binding affinity" = "#FC4E2A", "TFs abrogating binding affinity" = "#1D91C0"))+ theme_bw()+theme(text = element_text(size=20), axis.text.x = element_text(angle=90, hjust=1)) +scale_x_discrete(limits=plot.signature.TF.summary.loss$signature)+ theme(legend.position = "none") dev.off()
TF.DBD in each cluster Figure 3
hc.cutree.for.TF.clustering <- cutree(as.hclust(hm$colDendrogram), 1:1164) cluster.A.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==2,4]) cluster.B.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==1,4]) cluster.C.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==4,4]) cluster.D.TF <- names(hc.cutree.for.TF.clustering[hc.cutree.for.TF.clustering[,4]==3,4]) cluster.A.TF <- data.frame(cluster.A.TF) cluster.A.TF$cluster <- "A" cluster.A.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.A.TF[,1],TF.uPBM.info$Gene)] cluster.B.TF <- data.frame(cluster.B.TF) cluster.B.TF$cluster <- "B" cluster.B.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.B.TF[,1],TF.uPBM.info$Gene)] cluster.C.TF <- data.frame(cluster.C.TF) cluster.C.TF$cluster <- "C" cluster.C.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.C.TF[,1],TF.uPBM.info$Gene)] cluster.D.TF <- data.frame(cluster.D.TF) cluster.D.TF$cluster <- "D" cluster.D.TF$TF.family <- TF.uPBM.info$DBD[match(cluster.D.TF[,1],TF.uPBM.info$Gene)] ##organize all clustering information together cluster.composition.summary <- data.frame(matrix(nrow=0,ncol=4)) for(cluster.name in c("cluster.A.TF","cluster.B.TF","cluster.C.TF", "cluster.D.TF")){ cluster <- get(cluster.name) colnames(cluster)[1] <- "TFname" cluster <- cluster[!duplicated(cluster$TFname),] cluster.composition.matrix <- data.frame(table(cluster$TF.family)) cluster.composition.matrix$cluster <- cluster.name cluster.composition.matrix$percentage <- cluster.composition.matrix$Freq/sum(cluster.composition.matrix$Freq) cluster.composition.summary <- rbind(cluster.composition.summary,cluster.composition.matrix) }
dbs <- listEnrichrDbs() dbs.selected <- c("Reactome_2016") for(signature in unartifact.signatures){ gain.TF.list <- TF.median.GRs$Gene[TF.median.GRs[,signature]>1] ##selected TFs with GR>1 PathwaySelection(gain.TF.list,0.005,"Reactome_2016") loss.TF.list <- TF.median.LRs$Gene[TF.median.LRs[,signature]>1] ##selected TFs with LR>1 PathwaySelection(loss.TF.list,0.005,"Reactome_2016") } pathways.summary.gain.binding <- data.frame(matrix(nrow=0,ncol=13)) pathways.summary.loss.binding <- data.frame(matrix(nrow=0,ncol=13)) for(signature in unartifact.signatures){ outname.enrichr.gain <- paste("filtered.median.enrichR.Reactome.gained.by.",signature,sep="") outname.enrichr.loss <- paste("filtered.median.enrichR.Reactome.lossed.by.",signature,sep="") gain.matrix <- get(outname.enrichr.gain) loss.matrix <- get(outname.enrichr.loss) if(nrow(gain.matrix)>0){ gain.matrix$Signature <- signature colnames(pathways.summary.gain.binding) <- colnames(gain.matrix) pathways.summary.gain.binding <- rbind(pathways.summary.gain.binding,gain.matrix) } if(nrow(loss.matrix)>0){ loss.matrix$Signature <- signature colnames(pathways.summary.loss.binding) <- colnames(loss.matrix) pathways.summary.loss.binding <- rbind(pathways.summary.loss.binding,loss.matrix) } } combined.pathways <- unique(c(pathways.summary.gain.binding$New.Term,pathways.summary.loss.binding$New.Term)) combined.dot.matrix.plot <- data.frame(matrix(nrow=length(combined.pathways),ncol=47)) row.names(combined.dot.matrix.plot) <- combined.pathways colnames(combined.dot.matrix.plot) <- unartifact.signatures combined.dot.matrix.plot[,] <- 0 for(i in 1:nrow(combined.dot.matrix.plot)){ if(length(which(pathways.summary.gain.binding$New.Term== row.names(combined.dot.matrix.plot)[i]))>0){ signature.list <- pathways.summary.gain.binding$Signature[which(pathways.summary.gain.binding$New.Term== row.names(combined.dot.matrix.plot)[i])] combined.dot.matrix.plot[i,signature.list] <- combined.dot.matrix.plot[i,signature.list] + 1 } if(length(which(pathways.summary.loss.binding$New.Term== row.names(combined.dot.matrix.plot)[i]))>0){ signature.list <- pathways.summary.loss.binding$Signature[which(pathways.summary.loss.binding$New.Term== row.names(combined.dot.matrix.plot)[i])] combined.dot.matrix.plot[i,signature.list] <- combined.dot.matrix.plot[i,signature.list] + 2 } } summary.matrix <- data.frame(combined.pathways) for(i in 1:nrow(summary.matrix)){ summary.matrix$freq[i] <- sum(combined.dot.matrix.plot[i,]!=0) } specific.pathway.order <- summary.matrix$combined.pathways[order(summary.matrix$freq,decreasing = T)] plot.dot.matrix <- melt(as.matrix(combined.dot.matrix.plot)) colnames(plot.dot.matrix) <- c("Pathway","Signature","value") mycol <- c("grey","tomato1", "dodgerblue", "darkorchid3") sum.of.pathways.both <- data.frame(unartifact.signatures) sum.of.pathways.gain <- data.frame(unartifact.signatures) sum.of.pathways.loss <- data.frame(unartifact.signatures) for(i in 1:length(unartifact.signatures)){ sum.of.pathways.both$freq[i] <- sum(combined.dot.matrix.plot[,i]==3) sum.of.pathways.loss$freq[i] <- sum(combined.dot.matrix.plot[,i]==2) sum.of.pathways.gain$freq[i] <- sum(combined.dot.matrix.plot[,i]==1) } for(i in 1:nrow(sum.of.pathways.both)){ signature <- sum.of.pathways.both$Signature[i] selected.matrix <- PCAWG_sigProfiler_SBS_signatures_in_samples[which(PCAWG_sigProfiler_SBS_signatures_in_samples[,signature]!=0),] sum.of.pathways.both$type.of.cancer[i] <- length(unique(selected.matrix$Cancer.Types)) } sum.of.pathways.both$Category <- "Both" sum.of.pathways.loss$Category <- "Loss" sum.of.pathways.gain$Category <- "Gain" colnames(sum.of.pathways.both) <- c("Signature","Freq","Category") colnames(sum.of.pathways.loss) <- c("Signature","Freq","Category") colnames(sum.of.pathways.gain) <- c("Signature","Freq","Category") sum.of.pathways <- rbind(sum.of.pathways.both,sum.of.pathways.loss,sum.of.pathways.gain) for(i in 1:nrow(sum.of.pathways)){ sum.of.pathways$Sum[i] <- sum(sum.of.pathways$Freq[which(sum.of.pathways$Signature==sum.of.pathways$Signature[i])]) } specific.signature.order <- unique(sum.of.pathways$Signature[order(sum.of.pathways$Sum,decreasing=T)])
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