Nothing

```
## Returns a matrix which each entry[g1, g2] represents the number of "events" between a gene g1 and another gene g2.
## There is an event when
## (1) gene "g1" is mutated in a patient x,
## (2) gene "g2" is a neighbor of "g1" in the influence graph, and
## (3) the expression level "g2" is an outlier in the patient x.
.buildAggregateBipartiteGraph <- function(G, patMutMatrix, patOutMatrix) {
bip <- matrix(0, nrow=dim(patMutMatrix)[2], ncol=dim(patOutMatrix)[2])
rownames(bip) <- colnames(patMutMatrix)
colnames(bip) <- colnames(patOutMatrix)
drivers <- colnames(patMutMatrix)
i <- 1
for (i in 1:length(drivers)) {
## name of genes which are neighbor of "g" (drivers[i]) in the influence graph
neighbor <- colnames(G)[which(G[drivers[i], ] == 1)]
## patients in which "g" is mutated
pats <- which(patMutMatrix[, drivers[i]] == 1)
if (length(pats) == 0) {
next
}
j <- 1
for (j in 1:length(pats)) {
## For each patient that "g" is mutated, first find all its outlier expression.
## pat_events is the intersect of these outliers and the neighbor of "g"
pat_events <- intersect(neighbor, colnames(patOutMatrix)[which(patOutMatrix[pats[j], ] == 1)])
if (length(pat_events) > 0) {
bip[drivers[i], pat_events] <- bip[drivers[i], pat_events] + 1
}
}
}
## post processing to remove disconnected (singelton) nodes
count_mutated <- rowSums(bip)
count_outlier <- colSums(bip)
del_row <- which(count_mutated == 0)
del_col <- which(count_outlier == 0)
if (length(del_row) > 0) {
bip <- bip[-del_row, ]
}
if (length(del_col) > 0) {
bip <- bip[, -del_col]
}
bip
}
## return a list of lists, which each of them gives the neighbors of the node
.neighborGraph <- function(G) {
res <- list()
rn <- rownames(G)
cn <- colnames(G)
## For each row, find all names of column that the corresponding entry is positive
i <- 1
for (i in 1:dim(G)[1]) {
res[[rn[i]]] <- cn[which(G[i, ] > 0)]
}
res
}
```

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