# R/simplify.R In birte: Bayesian Inference of Regulatory Influence on Expression (biRte)

#### Documented in proposeInteractionssimplify

```simplify = function(affinities, cutoff=0.9){
join.regulators = function(reg.type){
reg = names(affinities[[reg.type]])
genesets = sapply(affinities[[reg.type]], names)
all.genes = unique(unlist(sapply(affinities[[reg.type]], names)))
X = matrix(0, ncol=length(reg), nrow=length(all.genes))
dimnames(X) = list(all.genes, reg)
for(r in reg){
X[genesets[[r]], r] = 1
}
D = dist(t(X), method="binary")
hc = hclust(D, method="single")
plot(hc)
cl = cutree(hc, h=1-cutoff)
maxcl = max(cl)
cat("merging", length(reg), "regulators into", maxcl, "clusters\n")
a = lapply(1:maxcl, function(r){
g = unique(unlist(genesets[which(cl == r)]))
v = rep(1, length(g))
names(v) = g
v
})
names(a) = sapply(1:maxcl, function(r) paste(names(genesets)[which(cl == r)], sep="", collapse="_U_"))
a
}

reg.types = names(affinities)
affinities = lapply(names(affinities)[sapply(affinities, length) > 0], join.regulators)
names(affinities) = reg.types[sapply(affinities, length) > 0]
affinities
}

proposeInteractions = function(affinities, cutoff.lower=0.1, cutoff.upper=0.8){
affinities = c(affinities\$TF, affinities\$miRNA)
reg = names(affinities)
genesets = sapply(affinities, names)
all.genes = unique(unlist(sapply(affinities, names)))
X = matrix(0, ncol=length(reg), nrow=length(all.genes))
dimnames(X) = list(all.genes, reg)
for(r in reg){
X[genesets[[r]], r] = 1
}
Te = 1 - as.matrix(dist(t(X), method="binary"))
Te[lower.tri(Te)] = 0
take = which(Te > cutoff.lower & Te < cutoff.upper, arr.ind=TRUE)
cat("considering", NROW(take), "interaction terms\n")
a = lapply(1:NROW(take), function(i){
int = intersect(genesets[[take[i,1]]], genesets[[take[i,2]]])
v = rep(1, length(int))
names(v) = int
v
})
names(a) = sapply(1:NROW(take), function(i) paste(names(genesets)[take[i,1]], "_", names(genesets)[take[i,2]], sep=""))
a
}
```

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birte documentation built on May 2, 2019, 12:32 a.m.