R/extract_subpopulations.R

In cytominer: Methods for Image-Based Cell Profiling

utils::globalVariables(c("type", "cluster_id"))
#' Extract subpopulations.
#'
#' \code{extract_subpopulations} identifies clusters in the reference and
#' population sets and reports the frequency of points in each cluster for the
#' two sets.
#'
#' @param population tbl with grouping (metadata) and observation variables.
#' @param reference tbl with grouping (metadata) and observation variables. Columns of \code{population} and \code{reference} should be identical.
#' @param variables character vector specifying observation variables.
#' @param k scalar specifying number of clusters.
#'
#' @return list containing clusters centers (\code{subpop_centers}), two
#' normalized histograms specifying frequency of each clusters in population
#' and reference (\code{subpop_profiles}), and cluster prediction and distance to
#' the predicted cluster for all input data (\code{population_clusters} and
#' \code{reference_clusters}).

#' @examples
#' data <- tibble::tibble(
#'   Metadata_group = c(
#'     "control", "control", "control", "control",
#'     "experiment", "experiment", "experiment", "experiment"
#'   ),
#'   AreaShape_Area = c(10, 12, NA, 16, 8, 8, 7, 7),
#'   AreaShape_Length = c(2, 3, NA, NA, 4, 5, 1, 5)
#' )
#' variables <- c("AreaShape_Area", "AreaShape_Length")
#' population <- dplyr::filter(data, Metadata_group == "experiment")
#' reference <- dplyr::filter(data, Metadata_group == "control")
#' extract_subpopulations(
#'   population = population,
#'   reference = reference,
#'   variables = variables,
#'   k = 3
#' )
#' @importFrom magrittr %>%
#' @importFrom magrittr %<>%
#' @importFrom stats setNames
#' @importFrom rlang .data
#'
#' @export
#'
extract_subpopulations <-
  function(population,
           reference,
           variables,
           k) {
    data <-
      dplyr::bind_rows(
        population %>% dplyr::mutate(type = "population"),
        reference %>% dplyr::mutate(type = "reference")
      ) %>%
      tidyr::drop_na(dplyr::one_of(variables))

    kmeans_output <- data %>%
      dplyr::select(variables) %>%
      stats::kmeans(
        centers = k,
        iter.max = 5000,
        nstart = 10
      )

    find_dist_to_cluster <- function(x) {
      (rbind(
        x[, variables],
        kmeans_output$centers[x[["cluster_id"]][1], variables]
      ) %>%
        stats::dist() %>%
        as.matrix())[1, 2]
    }

    data %<>% dplyr::mutate(cluster_id = unname(kmeans_output$cluster))
    data %<>%
      dplyr::bind_cols(
        purrr::map_df(
          1:nrow(data),
          ~ dplyr::tibble(
            dist_to_cluster =
              find_dist_to_cluster(data[.x, ])
          )
        )
      )

    subpop_profiles <- data %>%
      dplyr::group_by(type, cluster_id) %>%
      dplyr::tally() %>%
      dplyr::group_by(type) %>%
      dplyr::rename(freq = "n") %>%
      dplyr::mutate(freq = .data$freq / sum(.data$freq)) %>%
      dplyr::ungroup() %>%
      tidyr::spread(key = "type", value = "freq", fill = 0)

    population_clusters <-
      data %>%
      dplyr::filter(type == "population") %>%
      dplyr::select(-type) %>%
      dplyr::select(-dplyr::one_of(variables))

    reference_clusters <-
      data %>%
      dplyr::filter(type == "reference") %>%
      dplyr::select(-type) %>%
      dplyr::select(-dplyr::one_of(variables))

    return(list(
      subpop_centers = kmeans_output$centers,
      subpop_profiles = subpop_profiles,
      population_clusters = population_clusters,
      reference_clusters = reference_clusters
    ))
  }