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clustering: Expose clusteriness parameters

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This commit is contained in:
Elias Projahn 2022-06-22 11:24:30 +02:00
parent c6ca93b009
commit ab545a415c
2 changed files with 49 additions and 26 deletions
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65
R/method_clustering.R
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@ -68,7 +68,9 @@ clusteriness <- function(data,
#' Process genes clustering their distance to telomeres.
#'
#' The result will be cached and can be reused for different presets, because
#' it is independent of the reference genes in use.
#' it is independent of the reference genes in use. Most parameters are exposed
#' for the [clusteriness()] function. See its documentation for more
#' information.
#'
#' @param id Unique ID for the method and its results.
#' @param name Human readable name for the method.
@ -80,8 +82,12 @@ clusteriness <- function(data,
#'
#' @export
clustering <- function(id = "clustering",
name = "Clustering",
description = "Clustering of genes") {
name = "Clustering",
description = "Clustering of genes",
span = 100000,
weight = 0.7,
n_clusters = NULL,
relation = NULL) {
method(
id = id,
name = name,
@ -90,33 +96,44 @@ clustering <- function(id = "clustering",
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
cached("clustering", c(species_ids, gene_ids), {
scores <- data.table(gene = gene_ids)
cached(
"clustering",
c(species_ids, gene_ids, span, weight, n_clusters, relation),
{ # nolint
scores <- data.table(gene = gene_ids)
# Prefilter the input data by species.
distances <- geposan::distances[species %chin% species_ids]
# Prefilter the input data by species.
distances <- geposan::distances[species %chin% species_ids]
genes_done <- 0
genes_total <- length(gene_ids)
genes_done <- 0
genes_total <- length(gene_ids)
# Perform the cluster analysis for one gene.
compute <- function(gene_id) {
data <- distances[gene == gene_id, distance]
score <- clusteriness(data)
# Perform the cluster analysis for one gene.
compute <- function(gene_id) {
data <- distances[gene == gene_id, distance]
genes_done <<- genes_done + 1
progress(genes_done / genes_total)
score <- clusteriness(
data,
span = span,
weight = weight,
n_clusters = n_clusters,
relation = relation
)
score
genes_done <<- genes_done + 1
progress(genes_done / genes_total)
score
}
scores[, score := compute(gene), by = gene]
result(
method = "clustering",
scores = scores
)
}
scores[, score := compute(gene), by = gene]
result(
method = "clustering",
scores = scores
)
})
)
}
)
}