# Oncoprint script? [closed]

Can someone walk me through what each individual component of this Oncoprint script: https://github.com/dakl/oncoprint/blob/master/R/oncoprint.R is doing? I'm new to R and I'm trying to learn the intricacies of the source script. I'm hoping to eventually be able to create heatmaps of my own, but I'm having issues coercing my data into a format that ggplot is able to read. For reference, I've also attached some test data I was provided from the script repository.

I'm trying to recreate the ggplot function on my own, but I'm having issues with specific variables not being defined when trying to call the ggplot function with the test data itself. Weirdly enough, when I copy the script over and store as a different function name, it stops working; however, only when I call the function as oncoprint does it produce a heatmap.

edit: having trouble plotting specific data (e.g. 5 samples) - I've considered using lapply but it doesn't seem to produce the right figure I'd like it to

M <- tcga_brca

keys=list(somatic="MUT", germline="GERMLINE", amp="AMP",
del="HOMDEL", upreg="UP", downreg="DOWN")
sortGenes=FALSE

#M
all <- melt(M, varnames = c("gene", "patient"), value.name = "alteration")
genes <- na.omit(unique(as.character(all$$gene))) patients <- na.omit(unique( as.character(all$$patient) ))

e <- data.frame(gene=NA, patient=NA, alteration=NA)
data <- list()
for( alteration in names(keys) ){
data[[alteration]] <- all[ grep(pattern = keys[[alteration]], all$$alteration) ,] if(nrow(data[[alteration]]) == 0 ) data[[alteration]] <- e data[[alteration]]$$gene <- factor(data[[alteration]]$$gene, levels=genes) data[[alteration]]$$gene_y <- 0
}
background <- as.data.frame(matrix(0, nc=length(patients), nr=length(genes)))
colnames(background) <- patients
background$gene <- genes background.m <<- melt(background, id.vars = "gene", variable.name = "patient") # http://stackoverflow.com/a/16486873/179444 # > when you add a new data set to a geom you need to use the # > data= argument. Or put the arguments in the proper order mapping=..., data=.... Take a look at the arguments for ?geom_line. # # sort genes, by name # 'y' in the df below is the actual y-values used in the plot so it sets the order gene_y_map <- data.frame(gene=genes, y=order(genes, decreasing = TRUE)) background.m$$gene_y <- gene_y_map$$y[ match( background.m$$gene, gene_y_map$$gene ) ] ## create numerical mutation matrix mutmat <- as.data.frame(matrix(0, nc=length(patients), nr=length(genes))) colnames(mutmat) <- patients rownames(mutmat) <- genes mutmat <- mutmat[rev(gene_y_map$$y),] # from https://github.com/gideonite/WIP/blob/gh-pages/oncoprint/MemoSort.js # // sorting order : amplification, deletion, mutation, mrna, rppa # // mutation > 0 # // amp > del > 0 mutmat <- incrementMatrix(M=mutmat, events = data$$amp, inc=128) mutmat <- incrementMatrix(M=mutmat, events = data$$del, inc=64) mutmat <- incrementMatrix(M=mutmat, events = data$$somatic, inc=32) mutmat <- incrementMatrix(M=mutmat, events = data$$germline, inc=16) mutmat <- incrementMatrix(M=mutmat, events = data$$upreg, inc=8) mutmat <- incrementMatrix(M=mutmat, events = data$downreg, inc=4)

mutmat <- memoSort(mutmat, sortGenes = sortGenes)
levels(background.m\$patient) <- colnames(mutmat)

for(gene in genes){
idx.somatic  <- which( data$$somatic$$gene == gene)
idx.germline <- which( data$$germline$$gene == gene)
if(length(idx.somatic) > 0 & length(idx.germline) > 0){
data$$somatic$$gene_y[idx.somatic] <- gene_y_map$$y[which(gene_y_map$$gene==gene)] + .25
data$$germline$$gene_y[idx.germline] <- gene_y_map$$y[which(gene_y_map$$gene==gene)] - .25
} else if(length(idx.somatic) > 0) {
data$$somatic$$gene_y[idx.somatic] <- gene_y_map$$y[which(gene_y_map$$gene==gene)]
}else if(length(idx.germline) > 0) {
data$$germline$$gene_y[idx.germline] <- gene_y_map$$y[which(gene_y_map$$gene==gene)]
}
}
data$$amp$$gene_y <- gene_y_map$$y[match(data$$amp$$gene, gene_y_map$$gene)]
data$$del$$gene_y <- gene_y_map$$y[match(data$$del$$gene, gene_y_map$$gene)]
data$$upreg$$gene_y <- gene_y_map$$y[match(data$$upreg$$gene, gene_y_map$$gene)]
data$$downreg$$gene_y <- gene_y_map$$y[match(data$$downreg$$gene, gene_y_map$$gene)]

square_w <- .9
square_h <- .4

ggplot(background.m, aes(x=patient, y=gene_y)) + geom_tile(fill="gray", colour="white", size=1.1) +
scale_y_continuous(breaks=unique(background.m$$gene_y), labels=unique(background.m$$gene)) +
geom_tile(data=data$$amp, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=.9, height=.9, fill="firebrick", colour=NA, size=2) + geom_tile(data=data$$del, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=.9, height=.9, fill="blue", colour=NA, size=2) +
geom_tile(data=data$$somatic, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=square_w, height=square_h, fill="forestgreen") + geom_tile(data=data$$germline, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=square_w, height=square_h, fill="purple", colour=NA) +
geom_tile(data=data$$upreg, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=.9, height=.9, fill=NA, colour="firebrick", size=2) + geom_tile(data=data$$downreg, aes(x=patient, y=gene_y), inherit.aes=FALSE, width=.9, height=.9, fill=NA, colour="dodgerblue", size=2) +
theme_minimal() + xlab("Sample") + ylab("Gene")


• Hi @JohnAbercrombie.. hmmm i dunno if this is the right type of question to ask here.. Maybe you can highlight one part where you are stuck and or one function and someone will definitely help you with it – StupidWolf May 22 at 21:25
• How would I go about coercing the test data into a format that would be readable in R? That's basically what I'm trying to figure out right now – John Abercrombie May 22 at 21:29
• something like in here? bioconductor.statistik.tu-dortmund.de/packages/3.8/bioc/… can you post an example of your data? – StupidWolf May 22 at 21:33
• github.com/dakl/oncoprint/blob/master/data/tcga_brca.rda I've edited my script so that it reads through properly, but now I have an issue where I'm confused as to how I can plot specific things in from the data set (e.g. 5 specific samples) - I will append my script in the questions – John Abercrombie May 23 at 3:02
• Digging into a source code is not trivial depending on how complex it is. I suggest you post a question that specifically addresses a certain point rather than asking for a walkthrough. – ATpoint May 23 at 11:11