mirror of
https://github.com/johrpan/geposan.git
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106 lines
3.2 KiB
R
106 lines
3.2 KiB
R
#' Create a new preset.
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#'
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#' A preset is used to specify which methods and inputs should be used for an
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#' analysis. Note that the genes to process should normally include the
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#' reference genes to be able to assess the results later. The genes will be
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#' filtered based on how many species have data for them. Genes which only have
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#' orthologs for less than 25% of the input species will be excluded from the
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#' preset and the analyis.
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#'
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#' Available methods are:
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#'
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#' - `clusteriness` How much the gene distances to the nearest telomere
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#' cluster across species.
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#' - `correlation` The mean correlation of gene distances to the nearest
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#' telomere across species.
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#' - `neural` Assessment by neural network trained on the reference genes.
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#' - `adjacency` Proximity to reference genes.
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#' - `proximity` Mean proximity to telomeres.
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#'
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#' Available optimization targets are:
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#'
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#' - `mean` Mean rank of the reference genes.
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#' - `median` Median rank of the reference genes.
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#' - `max` First rank of the reference genes.
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#' - `min` Last rank of the reference genes.
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#'
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#' @param methods Methods to apply.
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#' @param species_ids IDs of species to include.
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#' @param gene_ids IDs of genes to screen.
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#' @param reference_gene_ids IDs of reference genes to compare to.
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#' @param optimization_target Parameter of the reference genes that the ranking
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#' should be optimized for.
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#'
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#' @return The preset to use with [analyze()].
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#'
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#' @export
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preset <- function(methods = c(
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"clusteriness",
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"correlation",
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"neural",
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"adjacency",
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"proximity"
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),
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species_ids = NULL,
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gene_ids = NULL,
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reference_gene_ids = NULL,
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optimization_target = "mean_rank") {
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# Count included species per gene.
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genes_n_species <- geposan::distances[
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species %chin% species_ids,
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.(n_species = .N),
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by = "gene"
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]
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# Filter out genes with less than 25% existing orthologs.
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gene_ids_filtered <- genes_n_species[
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gene %chin% gene_ids &
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n_species >= 0.25 * length(species_ids),
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gene
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]
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# The included data gets sorted to be able to produce predictable hashes
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# for the object later.
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structure(
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list(
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methods = sort(methods),
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species_ids = sort(species_ids),
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gene_ids = sort(gene_ids_filtered),
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reference_gene_ids = sort(reference_gene_ids),
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optimization_target = optimization_target
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),
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class = "geposan_preset"
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)
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}
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#' S3 method to print a preset object.
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#'
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#' @param x The preset to print.
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#' @param ... Other parameters.
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#'
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#' @seealso [preset()]
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#'
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#' @export
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print.geposan_preset <- function(x, ...) {
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cat("geposan preset:")
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cat("\n Included methods: ")
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cat(x$methods, sep = ", ")
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cat(sprintf(
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"\n Input data: %i species, %i genes",
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length(x$species_ids),
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length(x$gene_ids)
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))
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cat(sprintf(
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"\n Comparison data: %i reference genes",
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length(x$reference_gene_ids)
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))
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cat(sprintf(
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"\n Optimization target: %s\n",
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x$optimization_target
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))
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invisible(x)
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}
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