R/sim_normal.R

Defines functions sim_normal

Documented in sim_normal

#' Simulate sequences for a given evolutionary tree, using a standard model
#' of sequence evolution along the branches. Code for this function was heavily
#' inspired by the function \code{simSeq} from the phangorn package.
#' @param x a phylogenetic tree \code{tree}, i.e. an object of class
#' \code{phylo}
#' @param l length of the sequence to simulate.
#' @param Q the rate matrix.
#' @param bf base frequencies.
#' @param rootseq a vector of length l containing the root sequence, other root
#' sequence is randomly generated.
#' @param rate mutation rate
#' @return list with four items \enumerate{
#' \item{alignment} Phydat object with the resulting alignment
#' \item{rootseq} the rootsequence used
#' \item{total_branch_substitutions} total number of substitutions accumulated
#' on the branches
#' \item{total_node_substitutions} total number of substitutions accumulated at
#' the nodes}
#' @author Klaus Schliep \email{klaus.schliep@@gmail.com}
#' @export
sim_normal <- function(x,
                       l = 1000,
                       Q = NULL,   # nolint
                       bf = NULL,
                       rootseq = NULL,
                       rate = 1) {


  levels <- c("a", "c", "g", "t")

  lbf <- length(levels)

  if (is.null(bf)) bf <- rep(1 / lbf, lbf)
  if (is.null(Q)) {
    Q <- rep(1, lbf * (lbf - 1) / 2) # nolint
  }
  if (is.matrix(Q)) Q <- Q[lower.tri(Q)]  # nolint
  # capital Q is retained to conform to mathematical notation on wikipedia
  # and in the literature

  eig <- phangorn::edQt(Q, bf)

  m <- length(levels)

  if (is.null(rootseq)) rootseq <- sample(levels, l, replace = TRUE, prob = bf)
  x <- stats::reorder(x)
  edge <- x$edge
  num_nodes <- max(edge)
  res <- matrix(NA, l, num_nodes)
  parent <- as.integer(edge[, 1])
  child <- as.integer(edge[, 2])
  root <- as.integer(parent[!match(parent, child, 0)][1])
  res[, root] <- rootseq
  tl <- x$edge.length

  total_branch_subs <- 0
  daughter_subs <- rep(0, length(parent))

  for (i in seq_along(tl)) {
    from <- parent[i]
    to <- child[i]
    P <- get_p_matrix(tl[i], eig, rate)  # nolint
    # capital P is retained to conform to mathematical notation on wikipedia
    # and in the literature

    for (j in 1:m) {
      ind <- res[, from] == levels[j]
      res[ind, to] <- sample(levels, sum(ind), replace = TRUE, prob = P[, j])
    }

    branch_subs <- sum(res[, from] != res[, to])
    total_branch_subs <- total_branch_subs + branch_subs
    daughter_subs[i] <- branch_subs
  }

  # now, given the daughter subs string, we need to calculate the total
  # accumulated divergence
  updated_subs <- calc_accumulated_substitutions(x, daughter_subs)

  phy_no_extinct <- geiger::drop.extinct(x)

  k <- length(x$tip.label)
  label <- c(x$tip.label, as.character((k + 1):num_nodes))
  colnames(res) <- label
  res <- res[, phy_no_extinct$tip.label, drop = FALSE]
  alignment_phydat <- phyDat.DNA(as.data.frame(res, stringsAsFactors = FALSE))

  output <- list("alignment" = alignment_phydat,
                 "root_seq" = rootseq,
                 "total_branch_substitutions" = updated_subs$total_branch_subs,
                 "total_node_substitutions" = updated_subs$total_node_subs,
                 "total_accumulated_substitutions" =
                     updated_subs$total_accumulated_substitutions)

 return(output)
}

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nodeSub documentation built on Aug. 19, 2022, 5:23 p.m.