/
treeRearrangement.R
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treeRearrangement.R
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nnin <- function(tree, n) {
attr(tree, "order") <- NULL
tree1 <- tree
tree2 <- tree
edge <- matrix(tree$edge, ncol = 2)
parent <- edge[, 1]
child <- tree$edge[, 2]
k <- min(parent) - 1
ind <- which(child > k)[n]
if (is.na(ind)) return(NULL)
p1 <- parent[ind]
p2 <- child[ind]
ind1 <- which(parent == p1)
ind1 <- ind1[ind1 != ind][1]
ind2 <- which(parent == p2)
e1 <- child[ind1]
e2 <- child[ind2[1]]
e3 <- child[ind2[2]]
tree1$edge[ind1, 2] <- e2
tree1$edge[ind2[1], 2] <- e1
tree2$edge[ind1, 2] <- e3
tree2$edge[ind2[2], 2] <- e1
if (!is.null(tree$edge.length)) {
tree1$edge.length[c(ind1, ind2[1])] <- tree$edge.length[c(ind2[1], ind1)]
tree2$edge.length[c(ind1, ind2[2])] <- tree$edge.length[c(ind2[2], ind1)]
}
tree1 <- reorder(tree1, "postorder")
tree2 <- reorder(tree2, "postorder")
result <- list(tree1, tree2)
result
}
## @aliases nni rNNI rSPR
#' Tree rearrangements.
#'
#' \code{nni} returns a list of all trees which are one nearest neighbor
#' interchange away. \code{rNNI} and \code{rSPR} are two methods which simulate
#' random trees which are a specified number of rearrangement apart from the
#' input tree. Both methods assume that the input tree is bifurcating. These
#' methods may be useful in simulation studies.
#'
#'
#' @param tree A phylogenetic \code{tree}, object of class \code{phylo}.
#' @param moves Number of tree rearrangements to be transformed on a tree. Can
#' be a vector
#' @param n Number of trees to be simulated.
#' @param k If defined just SPR of distance k are performed.
#' @return an object of class multiPhylo.
#' @author Klaus Schliep \email{klaus.schliep@@gmail.com}
#' @seealso \code{\link{allTrees}}, \code{\link{SPR.dist}}
#' @keywords cluster
#' @examples
#'
#' tree <- rtree(20, rooted = FALSE)
#' trees1 <- nni(tree)
#' trees2 <- rSPR(tree, 2, 10)
#'
#' @rdname nni
#' @export nni
nni <- function(tree) {
tip.label <- tree$tip.label
attr(tree, "order") <- NULL
k <- min(tree$edge[, 1]) - 1
n <- sum(tree$edge[, 2] > k)
result <- vector("list", 2 * n)
l <- 1
for (i in 1:n) {
tmp <- nnin(tree, i)
tmp[[1]]$tip.label <- tmp[[2]]$tip.label <- NULL
result[c(l, l + 1)] <- tmp
l <- l + 2
}
attr(result, "TipLabel") <- tip.label
class(result) <- "multiPhylo"
result
}
#' @rdname nni
#' @export
rNNI <- function(tree, moves = 1, n = length(moves)) {
k <- length(na.omit(match(tree$edge[, 2], tree$edge[, 1])))
k_nni <- function(tree, ch, pvector, moves = 1L) {
if(length(edges)>1) p2_sample <- sample(edges, moves, replace=TRUE)
else p2_sample <- rep(edges, moves)
r2_sample <- sample(2, moves, replace=TRUE)
for (i in seq_len(moves)) {
p2 <- p2_sample[i]
p1 <- pvector[p2]
ind1 <- ch[[p1]]
v1 <- ind1[ind1 != p2][1]
ind2 <- ch[[p2]]
r2 <- r2_sample[i]
v2 <- ind2[r2]
ind1[ind1 == v1] <- v2
ind2[r2] <- v1
pvector[v1] <- p2
pvector[v2] <- p1
ch[[p1]] <- ind1
ch[[p2]] <- ind2
}
edge[, 1] <- pvector[child]
neworder <- reorderRcpp(edge, nb.tip, nb.tip + 1L, 2L)
tree$edge <- edge[neworder, ]
if (!is.null(tree$edge.length)) {
tree$edge.length <- tree$edge.length[neworder]
}
attr(tree, "order") <- "postorder"
tree
}
edge <- tree$edge
parent <- edge[, 1]
child <- edge[, 2]
nb.tip <- Ntip(tree)
if (nb.tip < (4L - is.rooted(tree))) stop("Not enough edges for NNI rearrangements")
pvector <- integer(max(edge))
pvector[child] <- parent
ch <- Children(tree)
edges <- child[child %in% parent]
if (n == 1) {
trees <- tree
if (moves > 0) {
trees <- k_nni(tree, ch, pvector, moves = moves)
}
trees$tip.label <- tree$tip.label
}
else {
trees <- vector("list", n)
tip.label <- tree$tip.label
tree$tip.label <- NULL
if (length(moves) == 1) moves <- rep(moves, n)
for (j in seq_len(n)) {
tmp <- tree
if (moves[j] > 0) {
tmp <- k_nni(tree, ch, pvector, moves = moves[j])
}
tmp$tip.label <- NULL
trees[[j]] <- tmp
}
attr(trees, "TipLabel") <- tip.label
class(trees) <- "multiPhylo"
}
trees
}
#### SPR ####
dn <- function(x) {
# if (!is.binary(x) ) x <- multi2di(x, random = FALSE)
if (is.null(x$edge.length)) x$edge.length <- rep(1, nrow(x$edge))
else x$edge.length[] <- 1
dist.nodes(x)
}
#' @rdname nni
#' @export
rSPR <- function(tree, moves = 1, n = length(moves), k = NULL) {
if (n == 1) {
trees <- tree
for (i in 1:moves) trees <- kSPR(trees, k = k)
}
else {
trees <- vector("list", n)
if (length(moves) == 1) moves <- rep(moves, n)
for (j in 1:n) {
tmp <- tree
if (moves[j] > 0) {
for (i in 1:moves[j]) tmp <- kSPR(tmp, k = k)
}
tmp$tip.label <- NULL
trees[[j]] <- tmp
}
attr(trees, "TipLabel") <- tree$tip.label
class(trees) <- "multiPhylo"
}
trees
}
kSPR <- function(tree, k = NULL) {
if (Ntip(tree) < (4L - is.rooted(tree))) return(tree)
l <- length(tree$tip.label)
root <- getRoot(tree)
distN <- dn(tree)[-c(1:l), -c(1:l)]
distN[upper.tri(distN)] <- Inf
dN <- distN[lower.tri(distN)]
tab <- tabulate(dN)
tab[1] <- tab[1] * 2
tab[-1] <- tab[-1] * 8
if (is.null(k)) k <- seq_along(tab)
k <- na.omit( (seq_along(tab))[k])
if (length(k) > 1) k <- sample(seq_along(tab)[k], 1,
prob = tab[k] / sum(tab[k]))
if (k == 1) return(rNNI(tree, 1, 1))
index <- which(distN == k, arr.ind = TRUE) + l
m <- dim(index)[1]
if (m == 0) stop("k is chosen too big")
ind <- index[sample(m, 1), ]
s1 <- sample(c(1, 2), 1)
if (s1 == 1) res <- oneOf4(tree, ind[1], ind[2], sample(c(1, 2), 1),
sample(c(1, 2), 1), root)
if (s1 == 2) res <- oneOf4(tree, ind[2], ind[1], sample(c(1, 2), 1),
sample(c(1, 2), 1), root)
res
}
oneOf4 <- function(tree, ind1, ind2, from = 1, to = 1, root) {
if (!is.binary(tree))
stop("trees must be binary")
tree <- reroot(tree, ind2, FALSE)
kids1 <- Children(tree, ind1)
anc <- Ancestors(tree, ind1, "all")
l <- length(anc)
kids2 <- Children(tree, ind2)
kids2 <- kids2[kids2 != anc[l - 1]]
child <- tree$edge[, 2]
tmp <- numeric(max(tree$edge))
tmp[child] <- seq_along(child)
edge <- tree$edge
edge[tmp[kids1[-from]], 1] <- Ancestors(tree, ind1, "parent")
edge[tmp[kids2[to]], 1] <- ind1
edge[tmp[ind1]] <- ind2
tree$edge <- edge
attr(tree, "order") <- NULL
tree <- reroot(tree, root, FALSE)
tree
}