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Rename method source files

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Elias Projahn 2022-01-26 09:58:33 +01:00
parent ab61f08d0d
commit c633b17db6
12 changed files with 7 additions and 7 deletions
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120
R/adjacency.R
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@ -1,120 +0,0 @@
#' Find the densest value in the data.
#'
#' This function assumes that data represents a continuous variable and finds
#' a single value with the highest estimated density. This can be used to
#' estimate the mode of the data. If there is only one value that value is
#' returned. If multiple density maxima with the same density exist, their mean
#' is returned.
#'
#' @param data The input data.
#'
#' @return The densest value of data.
#'
#' @export
densest <- function(data) {
as.numeric(if (length(data) <= 0) {
NULL
} else if (length(data) == 1) {
data
} else {
density <- stats::density(data)
mean(density$x[density$y == max(density$y)])
})
}
#' Score genes based on their proximity to the reference genes.
#'
#' In this case, the distance data that is available for one gene is first
#' combined. The resulting value is compared to the reference genes and
#' determines the gene's score in relation to other genes.
#'
#' @param distance_estimate A function that will be used to summarize the
#' distance values for each gene. See [densest()] for the default
#' implementation.
#' @param summarize A function that will be used to combine the different
#' distances to the reference genes. By default [min()] is used. That means
#' the distance to the nearest reference gene will be scored.
#'
#' @return An object of class `geposan_method`.
#'
#' @seealso [species_adjacency()]
#'
#' @export
adjacency <- function(distance_estimate = densest, summarize = min) {
method(
id = "adjacency",
name = "Adjacency",
description = "Adjacency to reference genes",
function(preset, progress) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
cached(
"adjacency",
c(
species_ids,
gene_ids,
reference_gene_ids,
distance_estimate,
summarize
),
{ # nolint
# Filter distances by species and gene and summarize each
# gene's distance values using the estimation function.
data <- geposan::distances[
species %chin% species_ids & gene %chin% gene_ids,
.(distance = as.numeric(distance_estimate(distance))),
by = gene
]
# Compute the absolute value of the difference between the
# estimated distances of each gene to the reference genes.
compute_difference <- function(distance_value,
comparison_ids) {
differences <- data[
gene %chin% comparison_ids,
.(difference = abs(distance_value - distance))
]
summarize(differences$difference)
}
# Compute the differences to the reference genes.
data[
!gene %chin% reference_gene_ids,
difference := compute_difference(
distance,
reference_gene_ids
),
by = gene
]
progress(0.5)
# Exclude the reference gene itself when computing its
# difference.
data[
gene %chin% reference_gene_ids,
difference := compute_difference(
distance,
reference_gene_ids[reference_gene_ids != gene]
),
by = gene
]
# Compute the final score by normalizing the difference.
data[, score := 1 - difference / max(difference)]
progress(1.0)
result(
method = "adjacency",
scores = data[, .(gene, score)],
details = list(data = data)
)
}
)
}
)
}