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R/analyze.R

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+#' 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 = methods,
+        species_ids = species,
+        gene_ids = genes,
+        reference_gene_ids = reference_genes
+    )
+}
+
+#' Analyze by applying the specified preset.
+#'
+#' @param preset The preset to use which can be created using [preset()].
+#'
+#' @return A [data.table] with one row for each gene identified by it's ID
+#'   (`gene` column). The additional columns contain the resulting scores per
+#'   method and are named after the method IDs.
+#'
+#' @export
+analyze <- function(preset) {
+    # Available methods by ID.
+    #
+    # A method describes a way to perform a computation on gene distance data
+    # that results in a single score per gene. The function should accept the
+    # preset to apply as a single parameter (see [preset()]).
+    #
+    # The function should return a [data.table] with the following columns:
+    #
+    #  - `gene` Gene ID of the processed gene.
+    #  - `score` Score for the gene between 0.0 and 1.0.
+    methods <- list(
+        "clusteriness" = clusteriness,
+        "correlation" = correlation,
+        "proximity" = proximity,
+        "neural" = neural
+    )
+
+    results <- data.table(gene = genes$id)
+
+    for (method_id in preset$method_ids) {
+        method_results <- methods[[method_id]](distances, preset)
+        setnames(method_results, "score", method_id)
+
+        results <- merge(
+            results,
+            method_results,
+            by = "gene"
+        )
+    }
+
+    results
+}