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Add species adjacency method
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6 changed files with 202 additions and 14 deletions
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@ -24,5 +24,6 @@ export(plot_scores)
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export(preset)
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export(ranking)
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export(result)
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export(species_adjacency)
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export(validate)
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import(data.table)
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@ -24,16 +24,23 @@ densest <- function(data) {
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#' Score genes based on their proximity to the reference genes.
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#'
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#' @param estimate A function that will be used to summarize the distance
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#' values for each gene. See [densest()] for the default implementation.
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#' @param combination A function that will be used to combine the different
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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 [min()] is used. That means
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#' the distance to the nearest reference gene will be scored.
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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(estimate = densest, combination = min) {
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adjacency <- function(distance_estimate = densest, summarize = min) {
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method(
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id = "adjacency",
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name = "Adjacency",
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@ -49,15 +56,15 @@ adjacency <- function(estimate = densest, combination = min) {
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species_ids,
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gene_ids,
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reference_gene_ids,
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estimate,
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combination
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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(estimate(distance))),
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.(distance = as.numeric(distance_estimate(distance))),
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by = gene
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]
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@ -70,7 +77,7 @@ adjacency <- function(estimate = densest, combination = min) {
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.(difference = abs(distance_value - distance))
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]
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combination(differences$difference)
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summarize(differences$difference)
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}
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# Compute the differences to the reference genes.
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@ -36,7 +36,8 @@ all_methods <- function() {
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clustering(),
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correlation(),
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neural(),
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adjacency()
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adjacency(),
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species_adjacency()
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)
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}
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148
R/species_adjacency.R
Normal file
148
R/species_adjacency.R
Normal file
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@ -0,0 +1,148 @@
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#' Score genes based on their adjacency to the reference genes within species.
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#'
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#' For each gene and species, the method will first combine the gene's distances
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#' to the reference genes within that species. Afterwards, the results are
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#' summarized across species and determine the gene's score.
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#'
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#' @param distance_estimate Function for combining the distance differences
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#' within one species.
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#' @param summarize Function for summarizing the distance values across species.
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#'
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#' @return An object of class `geposan_method`.
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#'
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#' @seealso [adjacency()]
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#'
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#' @export
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species_adjacency <- function(distance_estimate = min,
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summarize = stats::median) {
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method(
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id = "species_adjacency",
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name = "Species adj.",
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description = "Species adjacency",
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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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"species_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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# Prefilter distances.
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data <- geposan::distances[
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species %chin% species_ids & gene %chin% gene_ids
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]
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progress_state <- 0.0
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progress_step <- 0.9 / length(species_ids)
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# Iterate through all species and find the distance
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# estimates within that species.
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for (species_id in species_ids) {
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# For all genes, compute the distance to one reference
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# gene at a time in one go.
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for (reference_gene_id in reference_gene_ids) {
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comparison_distance <- data[
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species == species_id &
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gene == reference_gene_id,
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distance
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]
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column <- quote(reference_gene_id)
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if (length(comparison_distance) != 1) {
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# If we don't have a comparison distance, we
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# can't compute a difference. This happens, if
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# the species doesn't have the reference gene.
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data[
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species == species_id &
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gene %chin% gene_ids,
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eval(column) := NA_integer_
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]
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} else {
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data[
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species == species_id &
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gene %chin% gene_ids,
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eval(column) :=
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abs(distance - comparison_distance)
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]
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}
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}
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# Combine the distances to the different reference genes
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# into one value using the provided function.
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data[
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species == species_id &
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gene %chin% gene_ids,
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combined_distance := as.numeric(
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distance_estimate(na.omit(
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# Convert the data.table subset into a
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# vector to get the correct na.omit
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# behavior.
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as.matrix(.SD)[1, ]
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))
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),
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.SDcols = reference_gene_ids,
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by = gene
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]
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progress_state <- progress_state + progress_step
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progress(progress_state)
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}
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progress(0.9)
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# Remove the distances between the reference genes.
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for (reference_gene_id in reference_gene_ids) {
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column <- quote(reference_gene_id)
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data[gene == reference_gene_id, eval(column) := NA]
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}
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# Recompute the combined distance for the reference genes.
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data[
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gene %chin% reference_gene_ids,
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combined_distance := as.numeric(
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distance_estimate(na.omit(as.matrix(.SD)[1, ]))
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),
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.SDcols = reference_gene_ids,
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by = list(species, gene)
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]
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# Combine the distances into one value.
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results <- data[,
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.(
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summarized_distances = as.numeric(
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summarize(na.omit(combined_distance))
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)
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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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results[
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,
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score := 1 - summarized_distances /
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max(summarized_distances)
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]
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progress(1.0)
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result(
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method = "species_adjacency",
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scores = results[, .(gene, score)],
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details = list(
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data = data,
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results = results
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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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}
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@ -4,13 +4,14 @@
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\alias{adjacency}
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\title{Score genes based on their proximity to the reference genes.}
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\usage{
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adjacency(estimate = densest, combination = min)
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adjacency(distance_estimate = densest, summarize = min)
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}
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\arguments{
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\item{estimate}{A function that will be used to summarize the distance
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values for each gene. See \code{\link[=densest]{densest()}} for the default implementation.}
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\item{distance_estimate}{A function that will be used to summarize the
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distance values for each gene. See \code{\link[=densest]{densest()}} for the default
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implementation.}
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\item{combination}{A function that will be used to combine the different
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\item{summarize}{A function that will be used to combine the different
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distances to the reference genes. By default \code{\link[=min]{min()}} is used. That means
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the distance to the nearest reference gene will be scored.}
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}
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@ -18,5 +19,10 @@ the distance to the nearest reference gene will be scored.}
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An object of class \code{geposan_method}.
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}
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\description{
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Score genes based on their proximity to the reference genes.
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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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\seealso{
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\code{\link[=species_adjacency]{species_adjacency()}}
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}
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25
man/species_adjacency.Rd
Normal file
25
man/species_adjacency.Rd
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@ -0,0 +1,25 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/species_adjacency.R
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\name{species_adjacency}
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\alias{species_adjacency}
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\title{Score genes based on their adjacency to the reference genes within species.}
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\usage{
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species_adjacency(distance_estimate = min, summarize = stats::median)
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}
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\arguments{
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\item{distance_estimate}{Function for combining the distance differences
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within one species.}
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\item{summarize}{Function for summarizing the distance values across species.}
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}
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\value{
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An object of class \code{geposan_method}.
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}
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\description{
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For each gene and species, the method will first combine the gene's distances
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to the reference genes within that species. Afterwards, the results are
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summarized across species and determine the gene's score.
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}
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\seealso{
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\code{\link[=adjacency]{adjacency()}}
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}
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