R/jaccard.R

In ddiez/scmisc: Miscellaneus Tools for the Analysis of Single Cell Data

#' compute_jaccard
#'
#' @param x object to compute jaccard index on.
#' @param col.x name of column.
#' @param col.y name of column.
#' @param ... arguments passed down to methods.
#'
#' @export
compute_jaccard <- function(x, ...) {
  UseMethod("compute_jaccard")
}

#' @rdname compute_jaccard
#' @export
compute_jaccard.SingleCellExperiment <- function(x, ...) {
  compute_jaccard(colData(x), ...)
}

#' @rdname compute_jaccard
#' @export
compute_jaccard.DataFrame <- function(x, ...) {
  compute_jaccard(as.data.frame(x), ...)
}

#' @rdname compute_jaccard
#' @export
compute_jaccard.data.frame <- function(x, col.x, col.y, ...) {
  if (missing(col.x)) stop("col.x is missing.")
  if (missing(col.y)) stop("col.y is missing.")

  d <- x |> select_at(.vars = c(col.x, col.y))
  d <- d |> group_by_at(.vars = c(col.x, col.y)) |> dplyr::count() |> ungroup()
  d <- d |> group_by_at(.vars = col.y) |>
    mutate(total_y = sum(.data$n)) |>
    group_by_at(.vars = col.x) |>
    mutate(total_x = sum(.data$n))
  d |>
    mutate(jaccard = 2 * .data$n / (.data$total_x + .data$total_y)) |>
    select_at(.vars = c(col.x, col.y, count =  "n", "jaccard"))
}