R/qubiclust.R

In QUBIC: An R package for qualitative biclustering in support of gene co-expression analyses

#' @describeIn QUBIC Performs a QUalitative BIClustering for a discret matrix.
#'
#' @usage qubiclust_d(x, c = 0.95, o = 100, f = 1,
#'         k = max(ncol(x) \%/\% 20, 2),
#'         type = 'default', P = FALSE, C = FALSE, verbose = TRUE,
#'         weight = NULL, seedbicluster = NULL)
qubiclust_d <- function(x, c = 0.95, o = 100, f = 1,
                         k = max(ncol(x)%/%20, 2), type = "default",
                         P = FALSE, C = FALSE, verbose = TRUE, weight = NULL, seedbicluster = NULL) {
  MYCALL <- match.call()
  S <- (type == "area")
  if(!is.null(weight)) {
    w <- Matrix::Matrix(data = 0, nrow = nrow(x), ncol = nrow(x), dimnames = list(rownames(x), rownames(x)))
    weight[] <- rank(weight, ties.method = "average")
    intersect_rows <- intersect(rownames(x), rownames(weight))
    w[intersect_rows, intersect_rows] <- weight[intersect_rows, intersect_rows]
    res <- .qubic_dw(x, c, o, f, k, P, S, C, verbose, w)
  } else if(!is.null(seedbicluster)) {
    if (seedbicluster@Number >= 1)
      res <- .qubic_de(x, c, verbose, seedbicluster@RowxNumber, seedbicluster@NumberxCol)
    else
      return(seedbicluster)
  } else res <- .qubic_d(x, c, o, f, k, P, S, C, verbose)
  return(biclust::BiclustResult(as.list(MYCALL), matrix(unlist(res["RowxNumber"]), ncol = as.numeric(res["Number"]), byrow = FALSE),
                                matrix(unlist(res["NumberxCol"]), nrow = as.numeric(res["Number"]), byrow = FALSE), as.numeric(res["Number"]),
                                res["info"]))
}

#' @describeIn QUBIC Performs a QUalitative BIClustering.
#'
#' @usage qubiclust(x, r = 1L, q = 0.06, c = 0.95, o = 100, f = 1,
#'         k = max(ncol(x) \%/\% 20, 2),
#'         type = 'default', P = FALSE, C = FALSE, verbose = TRUE,
#'         weight = NULL, seedbicluster = NULL)
qubiclust <- function(x, r = 1L, q = 0.06, c = 0.95, o = 100, f = 1,
                       k = max(ncol(x)%/%20, 2), type = "default",
                       P = FALSE, C = FALSE, verbose = TRUE, weight = NULL, seedbicluster = NULL) {
  x_d <- qudiscretize(x, r, q)
  return(qubiclust_d(x_d, c, o, f, k, type, P, C, verbose, weight, seedbicluster))
}