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preset: Turn into S3 class

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Elias Projahn 2021-11-03 14:17:39 +01:00
parent 6494ae8200
commit 55958e0d85
8 changed files with 106 additions and 43 deletions
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44
R/analyze.R
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@ -1,36 +1,6 @@
#' Create a new preset.
#'
#' 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.
#'
#' Available methods are:
#'
#' - `clusteriness` How much the gene distances cluster across species.
#' - `correlation` The mean correlation with the reference genes.
#' - `proximity` Mean proximity to telomeres.
#' - `neural` Assessment by neural network.
#'
#' @param methods IDs of methods to apply.
#' @param species IDs of species to include.
#' @param genes IDs of genes to screen.
#' @param reference_genes IDs of reference genes to compare to.
#'
#' @return The preset to use with [analyze()].
#'
#' @export
preset <- function(methods, species, genes, reference_genes) {
list(
method_ids = sort(methods),
species_ids = sort(species),
gene_ids = sort(genes),
reference_gene_ids = sort(reference_genes)
)
}
#' Analyze by applying the specified preset.
#'
#' @param preset The preset to use which can be created using [preset()].
#' @param preset The preset to use which should be created using [preset()].
#' @param progress A function to be called for progress information. The
#' function should accept a number between 0.0 and 1.0 for the current
#' progress.
@ -41,6 +11,10 @@ preset <- function(methods, species, genes, reference_genes) {
#'
#' @export
analyze <- function(preset, progress = NULL) {
if (class(preset) != "geposan_preset") {
stop("Preset is invalid. Use geposan::preset() to create one.")
}
# Available methods by ID.
#
# A method describes a way to perform a computation on gene distance data
@ -64,9 +38,11 @@ analyze <- function(preset, progress = NULL) {
method_count <- length(preset$method_ids)
results <- data.table(gene = preset$gene_ids)
for (method_id in preset$method_ids) {
method_progress <- if (!is.null(progress)) function(p) {
progress(total_progress + p / method_count)
for (method_id in preset$methods) {
method_progress <- if (!is.null(progress)) {
function(p) {
progress(total_progress + p / method_count)
}
}
method_results <- methods[[method_id]](preset, method_progress)