mirror of
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114 lines
3.6 KiB
R
114 lines
3.6 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. Afterwards, species
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#' that still have many missing genes will also be excluded. See the different
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#' method functions for the available methods: [clustering()], [correlation()],
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#' [distance()], [neural()] and [random_forest()].
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#'
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#' @param reference_gene_ids IDs of reference genes to compare to.
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#' @param methods List of 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 species_requirement The proportion of species a gene has to have
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#' orthologs in in order for the gene to qualify.
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#' @param gene_requirement The proportion of genes that a species has to have
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#' in order for the species to be included in the analysis.
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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(reference_gene_ids,
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methods = all_methods(),
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species_ids = geposan::species$id,
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gene_ids = geposan::genes$id,
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species_requirement = 0.25,
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gene_requirement = 0.5) {
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# Prefilter distances.
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distances <- geposan::distances[
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species %chin% species_ids & gene %chin% gene_ids
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]
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# Count included species per gene.
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genes_n_species <- distances[, .(n_species = .N), by = "gene"]
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# Filter out genes with less too few existing orthologs.
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gene_ids_filtered <- genes_n_species[
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n_species >= species_requirement * length(species_ids),
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gene
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]
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# Count included genes per species.
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species_n_genes <- distances[, .(n_genes = .N), by = "species"]
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# Filter out species that have too few of the genes.
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species_ids_filtered <- species_n_genes[
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n_genes >= gene_requirement * length(gene_ids_filtered),
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species
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]
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reference_gene_ids_excluded <- reference_gene_ids[
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!reference_gene_ids %chin% gene_ids_filtered
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]
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if (length(reference_gene_ids_excluded > 0)) {
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warning(paste0(
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"The following reference gene IDs are excluded from the preset ",
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"because they don't have enough data: ",
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paste(reference_gene_ids_excluded, collapse = ", ")
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))
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}
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reference_gene_ids_included <- reference_gene_ids[
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reference_gene_ids %chin% gene_ids_filtered
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]
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if (length(reference_gene_ids_included) < 1) {
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stop(paste0(
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"There has to be at least one reference gene for the preset to be ",
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"valid. Please note that some methods may require more reference ",
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"genes."
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))
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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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reference_gene_ids = sort(reference_gene_ids_included),
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methods = methods,
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species_ids = sort(species_ids_filtered),
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gene_ids = sort(gene_ids_filtered)
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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(sprintf(
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paste0(
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"geposan preset:",
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"\n Reference genes: %i",
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"\n Included methods: %s",
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"\n Number of species: %i",
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"\n Number of genes: %i",
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"\n"
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),
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length(x$reference_gene_ids),
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paste(sapply(x$methods, function(m) m$id), collapse = ", "),
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length(x$species_ids),
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length(x$gene_ids)
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))
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invisible(x)
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}
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