preset: Turn into S3 class

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(print,geposan_preset)
 export(analyze)
 export(optimize_weights)
 export(preset)

R/analyze.R

@@ -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
@@ -64,10 +38,12 @@ analyze <- function(preset, progress = NULL) {
         method_count <- length(preset$method_ids)
         results <- data.table(gene = preset$gene_ids)
 
-        for (method_id in preset$method_ids) {
-            method_progress <- if (!is.null(progress)) function(p) {
+        for (method_id in preset$methods) {
+            method_progress <- if (!is.null(progress)) {
+                function(p) {
                     progress(total_progress + p / method_count)
                 }
+            }
 
             method_results <- methods[[method_id]](preset, method_progress)
             setnames(method_results, "score", method_id)

R/preset.R

@@ -0,0 +1,66 @@
+#' 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 Methods to apply.
+#' @param species_ids IDs of species to include.
+#' @param gene_ids IDs of genes to screen.
+#' @param reference_gene_ids IDs of reference genes to compare to.
+#'
+#' @return The preset to use with [analyze()].
+#'
+#' @export
+preset <- function(methods = c(
+                       "clusteriness",
+                       "correlation",
+                       "neural",
+                       "proximity"
+                   ),
+                   species_ids = NULL,
+                   gene_ids = NULL,
+                   reference_gene_ids = NULL) {
+    # 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),
+            reference_gene_ids = sort(reference_gene_ids)
+        ),
+        class = "geposan_preset"
+    )
+}
+
+#' S3 method to print a preset object.
+#'
+#' @seealso [preset()]
+#'
+#' @export
+print.geposan_preset <- function(preset, ...) {
+    cat("geposan preset:")
+    cat("\n  Included methods: ")
+    cat(preset$method_ids, sep = ", ")
+
+    cat(sprintf(
+        "\n  Input data: %i species, %i genes",
+        length(preset$species_ids),
+        length(preset$gene_ids)
+    ))
+
+    cat(sprintf(
+        "\n  Comparison data: %i reference genes\n",
+        length(preset$reference_gene_ids)
+    ))
+
+    invisible(preset)
+}

man/analyze.Rd

@@ -7,7 +7,7 @@
 analyze(preset, progress = NULL)
 }
 \arguments{
-\item{preset}{The preset to use which can be created using \code{\link[=preset]{preset()}}.}
+\item{preset}{The preset to use which should be created using \code{\link[=preset]{preset()}}.}
 
 \item{progress}{A function to be called for progress information. The
 function should accept a number between 0.0 and 1.0 for the current

man/distances.Rd

@@ -5,10 +5,11 @@
 \alias{distances}
 \title{Information on gene positions across species.}
 \format{
-A \link{data.table} with 1390730 rows and 3 variables:
+A \link{data.table} with 1506182 rows and 4 variables:
 \describe{
 \item{species}{Species ID}
 \item{gene}{Gene ID}
+\item{position}{Gene start position}
 \item{distance}{Distance to nearest telomere}
 }
 }

man/preset.Rd

@@ -1,19 +1,24 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/analyze.R
+% Please edit documentation in R/preset.R
 \name{preset}
 \alias{preset}
 \title{Create a new preset.}
 \usage{
-preset(methods, species, genes, reference_genes)
+preset(
+  methods = c("clusteriness", "correlation", "neural", "proximity"),
+  species_ids = NULL,
+  gene_ids = NULL,
+  reference_gene_ids = NULL
+)
 }
 \arguments{
-\item{methods}{IDs of methods to apply.}
+\item{methods}{Methods to apply.}
 
-\item{species}{IDs of species to include.}
+\item{species_ids}{IDs of species to include.}
 
-\item{genes}{IDs of genes to screen.}
+\item{gene_ids}{IDs of genes to screen.}
 
-\item{reference_genes}{IDs of reference genes to compare to.}
+\item{reference_gene_ids}{IDs of reference genes to compare to.}
 }
 \value{
 The preset to use with \code{\link[=analyze]{analyze()}}.

man/print.geposan_preset.Rd

@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preset.R
+\name{print.geposan_preset}
+\alias{print.geposan_preset}
+\title{S3 method to print a preset object.}
+\usage{
+\method{print}{geposan_preset}(preset, ...)
+}
+\description{
+S3 method to print a preset object.
+}
+\seealso{
+\code{\link[=preset]{preset()}}
+}

man/species.Rd

@@ -5,7 +5,7 @@
 \alias{species}
 \title{Information on included species from the Ensembl database.}
 \format{
-A \link{data.table} with 91 rows and 2 variables:
+A \link{data.table} with 99 rows and 2 variables:
 \describe{
 \item{id}{Unique species ID}
 \item{name}{Human readable species name}