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cumOverlap <- function(ol1, ol2)
# Cumulative overlap analysis.
# Test whether two ordered lists of IDs are significantly overlapped.
# Di Wu and Gordon Smyth
# Createdin 2007. Last modified 20 Sep 2018
{
# Check for duplicates
if(anyDuplicated(ol1)) stop("Duplicate IDs found in ol1")
if(anyDuplicated(ol2)) stop("Duplicate IDs found in ol2")
# Reduce to IDs found in both lists
m <- match(ol1,ol2)
redo <- FALSE
if(anyNA(m)) {
ol1 <- ol1[!is.na(m)]
redo <- TRUE
}
m2 <- match(ol2,ol1)
if(anyNA(m2)) {
ol2 <- ol2[!is.na(m2)]
redo <- TRUE
}
# Match ol1 to ol2
if(redo) m <- match(ol1,ol2)
# Count overlaps
ngenes <- length(ol1)
if(ngenes == 0L) return(list(n.total=0L))
i <- noverlap <- 1:ngenes
for (j in i) noverlap[j] <- sum(m[1:j] <= j)
# Hypergeometric p-valules
p <- phyper(noverlap-0.5,m=i,n=ngenes-i,k=i,lower.tail=FALSE)
# Directed Bonferroni adjustment, starting from top of list
p.b <- p*i
nmin <- which.min(p.b)
p.b <- pmin(p.b,1)
# Which ids contribute to overlap?
idoverlap <- ol1[which(m[1:nmin] <= nmin)]
list(n.total=ngenes,n.min=nmin,p.min=p.b[nmin],n.overlap=noverlap,id.overlap=idoverlap,p.value=p,adj.p.value=p.b)
}
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