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# Generate heat maps showing similarity of mass spec experiments
# Overall method:
# 1) load several mass spec experiments
# 2) Extract set of all observed Uniprot IDs
# 3) Assign score to each ID per each file
# a) either just binary (present/absent)
# b) or by Mascot score (-1 == missing)
# 4) Generate clustering/heat map of resulting table, somehow or other.
makeTable <- function(samples,useScore=F) {
ids <- vector()
for (sample in samples) {
ids <- c(ids,sample$data$uniprot)
}
ids <- unique(ids)
tbl <- data.frame(ids)
rownames(tbl) <- ids
for (sample in samples) {
if (useScore) {
map <- match(ids,sample$data$uniprot)
tbl[[sample$filename]] <- sample$data$score[map]
if (sum(is.na(map)) > 0) {
tbl[[sample$filename]][is.na(tbl[[sample$filename]])] <- 0
}
} else {
tbl[[sample$filename]] <- as.numeric(ids %in% sample$data$uniprot)
}
}
tbl$ids <- NULL
return(tbl)
}
msarc.plotHeatmap <- function(msalist,method="euclidean",useScore=T,...) {
tbl <- makeTable(msalist,useScore=useScore)
tbl <- data.matrix(tbl)
d = dist(t(tbl),diag=T,upper=T,method=method)
heatmap.2(as.matrix(d),margins=c(14,14),...)
return(invisible(d))
}
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