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119 lines
3.6 KiB
R
119 lines
3.6 KiB
R
#' Find the densest value in the data.
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#'
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#' This function assumes that data represents a continuous variable and finds
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#' a single value with the highest estimated density. This can be used to
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#' estimate the mode of the data. If there is only one value that value is
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#' returned. If multiple density maxima with the same density exist, their mean
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#' is returned.
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#'
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#' @param data The input data.
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#'
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#' @return The densest value of data.
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#'
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#' @export
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densest <- function(data) {
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as.numeric(if (length(data) <= 0) {
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NULL
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} else if (length(data) == 1) {
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data
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} else {
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density <- stats::density(data)
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mean(density$x[density$y == max(density$y)])
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})
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}
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#' Score genes based on their proximity to the reference genes.
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#'
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#' In this case, the distance data that is available for one gene is first
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#' combined. The resulting value is compared to the reference genes and
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#' determines the gene's score in relation to other genes.
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#'
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#' @param distance_estimate A function that will be used to summarize the
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#' distance values for each gene. See [densest()] for the default
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#' implementation.
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#' @param summarize A function that will be used to combine the different
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#' distances to the reference genes. By default [stats::median()] is used.
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#'
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#' @return An object of class `geposan_method`.
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#'
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#' @seealso [species_adjacency()]
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#'
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#' @export
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adjacency <- function(distance_estimate = densest, summarize = stats::median) {
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method(
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id = "adjacency",
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name = "Adjacency",
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description = "Adjacency to reference genes",
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function(preset, progress) {
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species_ids <- preset$species_ids
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gene_ids <- preset$gene_ids
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reference_gene_ids <- preset$reference_gene_ids
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cached(
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"adjacency",
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c(
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species_ids,
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gene_ids,
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reference_gene_ids,
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distance_estimate,
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summarize
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),
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{ # nolint
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# Filter distances by species and gene and summarize each
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# gene's distance values using the estimation function.
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data <- geposan::distances[
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species %chin% species_ids & gene %chin% gene_ids,
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.(distance = as.numeric(distance_estimate(distance))),
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by = gene
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]
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# Compute the absolute value of the difference between the
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# estimated distances of each gene to the reference genes.
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compute_difference <- function(distance_value,
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comparison_ids) {
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differences <- data[
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gene %chin% comparison_ids,
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.(difference = abs(distance_value - distance))
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]
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summarize(differences$difference)
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}
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# Compute the differences to the reference genes.
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data[
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!gene %chin% reference_gene_ids,
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difference := compute_difference(
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distance,
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reference_gene_ids
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),
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by = gene
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]
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progress(0.5)
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# Exclude the reference gene itself when computing its
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# difference.
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data[
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gene %chin% reference_gene_ids,
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difference := compute_difference(
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distance,
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reference_gene_ids[reference_gene_ids != gene]
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),
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by = gene
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]
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# Compute the final score by normalizing the difference.
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data[, score := 1 - difference / max(difference)]
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progress(1.0)
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result(
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method = "adjacency",
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scores = data[, .(gene, score)],
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details = list(data = data)
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)
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}
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)
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}
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)
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}
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