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preset: Remove min_n_species customization

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This commit is contained in:
Elias Projahn 2021-11-18 12:30:19 +01:00
parent 33056bfa40
commit de1c1ed40e
3 changed files with 48 additions and 31 deletions
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12
R/analyze.R
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@ -75,17 +75,7 @@ analyze <- function(preset, progress = NULL) {
total_progress <- total_progress + 1 / method_count
}
# Count included species from the preset per gene.
genes_n_species <- geposan::distances[
species %chin% preset$species_ids,
.(n_species = .N),
by = "gene"
]
setkey(genes_n_species, "gene")
# Return the results for genes with enough species.
results[genes_n_species[gene, n_species] >= preset$min_n_species]
results
})
if (!is.null(progress)) {

42
R/preset.R
View file

@ -2,20 +2,28 @@
#'
#' A preset is used to specify which methods and inputs should be used for an
#' analysis. Note that the genes to process should normally include the
#' reference genes to be able to assess the results later.
#' reference genes to be able to assess the results later. The genes will be
#' filtered based on how many species have data for them. Genes which only have
#' orthologs for less than 25% of the input species will be excluded from the
#' preset and the analyis.
#'
#' Available methods are:
#'
#' - `clusteriness` How much the gene distances cluster across species.
#' - `correlation` The mean correlation with the reference genes.
#' - `clusteriness` How much the gene distances to the nearest telomere
#' cluster across species.
#' - `clusteriness_positions` The same as `clusteriness` but using absolute
#' gene positions instead of distances.
#' - `correlation` The mean correlation of gene distances to the nearest
#' telomere across species.
#' - `correlation_positions` Correlation using position data.
#' - `neural` Assessment by neural network trained using distances.
#' - `neural_positions` Assessment by neural network trained using absolute
#' position data.
#' - `proximity` Mean proximity to telomeres.
#' - `neural` Assessment by neural network.
#'
#' @param methods Methods to apply.
#' @param species_ids IDs of species to include.
#' @param gene_ids IDs of genes to screen.
#' @param min_n_species Minimum number of orthologs that a gene should have to
#' be included in the analysis.
#' @param reference_gene_ids IDs of reference genes to compare to.
#'
#' @return The preset to use with [analyze()].
@ -23,22 +31,36 @@
#' @export
preset <- function(methods = c(
"clusteriness",
"clusteriness_positions",
"correlation",
"correlation_positions",
"neural",
"neural_positions",
"proximity"
),
species_ids = NULL,
gene_ids = NULL,
min_n_species = 10,
reference_gene_ids = NULL) {
# Count included species per gene.
genes_n_species <- geposan::distances[
species %chin% species_ids,
.(n_species = .N),
by = "gene"
]
# Filter out genes with less than 25% existing orthologs.
gene_ids_filtered <- genes_n_species[
n_species >= 0.25 * length(species_ids),
gene
]
# The included data gets sorted to be able to produce predictable hashes
# for the object later.
structure(
list(
methods = sort(methods),
species_ids = sort(species_ids),
gene_ids = sort(gene_ids),
min_n_species = as.numeric(min_n_species),
gene_ids = sort(gene_ids_filtered),
reference_gene_ids = sort(reference_gene_ids)
),
class = "geposan_preset"
@ -64,8 +86,6 @@ print.geposan_preset <- function(x, ...) {
length(x$gene_ids)
))
cat(sprintf("\n Species per gene: \u2265 %i", x$min_n_species))
cat(sprintf(
"\n Comparison data: %i reference genes\n",
length(x$reference_gene_ids)