analyze: Add optimization

R/analyze.R

@@ -9,9 +9,8 @@
 #'   items:
 #'   \describe{
 #'     \item{`preset`}{The preset that was used.}
-#'     \item{`results`}{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.}
+#'     \item{`weights`}{The optimal weights for ranking the reference genes.}
+#'     \item{`ranking`}{The optimal ranking created using the weights.}
 #'   }
 #'
 #' @export
@@ -75,10 +74,25 @@ analyze <- function(preset, progress = NULL) {
             total_progress <- total_progress + 1 / method_count
         }
 
+        results <- structure(
+            results,
+            class = c("geposan_results", class(results))
+        )
+
+        weights <- optimal_weights(
+            results,
+            preset$methods,
+            preset$reference_gene_ids,
+            target = preset$optimization_target
+        )
+
+        ranking <- ranking(results, weights)
+
         structure(
             list(
                 preset = preset,
-                results = results
+                weights = weights,
+                ranking = ranking
             ),
             class = "geposan_analysis"
         )

R/preset.R

@@ -21,10 +21,18 @@
 #'    position data.
 #'  - `proximity` Mean proximity to telomeres.
 #'
+#' Available optimization targets are:
+#'
+#'  - `mean` Mean rank of the reference genes.
+#'  - `max` First rank of the reference genes.
+#'  - `min` Last rank of the reference genes.
+#'
 #' @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.
+#' @param optimization_target Parameter of the reference genes that the ranking
+#'   should be optimized for.
 #'
 #' @return The preset to use with [analyze()].
 #'
@@ -40,7 +48,8 @@ preset <- function(methods = c(
                    ),
                    species_ids = NULL,
                    gene_ids = NULL,
-                   reference_gene_ids = NULL) {
+                   reference_gene_ids = NULL,
+                   optimization_target = "mean_rank") {
     # Count included species per gene.
     genes_n_species <- geposan::distances[
         species %chin% species_ids,
@@ -61,7 +70,8 @@ preset <- function(methods = c(
             methods = sort(methods),
             species_ids = sort(species_ids),
             gene_ids = sort(gene_ids_filtered),
-            reference_gene_ids = sort(reference_gene_ids)
+            reference_gene_ids = sort(reference_gene_ids),
+            optimization_target = optimization_target
         ),
         class = "geposan_preset"
     )
@@ -87,9 +97,14 @@ print.geposan_preset <- function(x, ...) {
     ))
 
     cat(sprintf(
-        "\n  Comparison data: %i reference genes\n",
+        "\n  Comparison data: %i reference genes",
         length(x$reference_gene_ids)
     ))
 
+    cat(sprintf(
+        "\n  Optimization target: %s\n",
+        x$optimization_target
+    ))
+
     invisible(x)
 }

R/ranking.R

@@ -13,11 +13,14 @@
 #'
 #' @export
 ranking <- function(analysis, weights) {
-    if (!"geposan_analysis" %chin% class(analysis)) {
+    if ("geposan_analysis" %chin% class(analysis)) {
+        ranking <- copy(analysis$ranking)
+    } else if ("geposan_results" %chin% class(analysis)) {
+        ranking <- copy(analysis)
+    } else {
         stop("Invalid analyis. Use geposan::analyze().")
     }
 
-    ranking <- copy(analysis$results)
     ranking[, score := 0.0]
 
     for (method in names(weights)) {
@@ -36,7 +39,7 @@ ranking <- function(analysis, weights) {
 
     structure(
         ranking,
-        class = c("geposan_ranking", "geposan_analysis", class(ranking))
+        class = c("geposan_ranking", "geposan_results", class(ranking))
     )
 }
 
@@ -57,24 +60,13 @@ ranking <- function(analysis, weights) {
 #' @export
 optimal_weights <- function(analysis, methods, reference_gene_ids,
                             target = "mean") {
-    if (!"geposan_analysis" %chin% class(analysis)) {
+    if (!any(c("geposan_analysis", "geposan_results") %chin% class(analysis))) {
         stop("Invalid analyis. Use geposan::analyze().")
     }
 
-    # Create the named list from the factors vector.
-    weights <- function(factors) {
-        result <- NULL
-
-        mapply(function(method, factor) {
-            result[[method]] <<- factor
-        }, methods, factors)
-
-        result
-    }
-
     # Compute the target rank of the reference genes when applying the weights.
     target_rank <- function(factors) {
-        data <- ranking(analysis, weights(factors))
+        data <- ranking(analysis, as.list(factors))
 
         result <- data[gene %chin% reference_gene_ids, if (target == "min") {
             min(rank)
@@ -91,10 +83,10 @@ optimal_weights <- function(analysis, methods, reference_gene_ids,
         }
     }
 
-    factors <- stats::optim(
-        rep(0.0, length(methods)),
-        target_rank
-    )$par
+    initial_factors <- rep(1.0, length(methods))
+    names(initial_factors) <- methods
 
-    weights(factors / max(abs(factors)))
+    optimal_factors <- stats::optim(initial_factors, target_rank)$par
+
+    as.list(optimal_factors / max(abs(optimal_factors)))
 }

man/analyze.Rd

@@ -18,9 +18,8 @@ An object containing the results of the analysis with the following
 items:
 \describe{
 \item{\code{preset}}{The preset that was used.}
-\item{\code{results}}{A \link{data.table} with one row for each gene identified by
-it's ID (\code{gene} column). The additional columns contain the resulting
-scores per method and are named after the method IDs.}
+\item{\code{weights}}{The optimal weights for ranking the reference genes.}
+\item{\code{ranking}}{The optimal ranking created using the weights.}
 }
 }
 \description{

man/preset.Rd

@@ -9,7 +9,8 @@ preset(
     "correlation_positions", "neural", "neural_positions", "proximity"),
   species_ids = NULL,
   gene_ids = NULL,
-  reference_gene_ids = NULL
+  reference_gene_ids = NULL,
+  optimization_target = "mean_rank"
 )
 }
 \arguments{
@@ -20,6 +21,9 @@ preset(
 \item{gene_ids}{IDs of genes to screen.}
 
 \item{reference_gene_ids}{IDs of reference genes to compare to.}
+
+\item{optimization_target}{Parameter of the reference genes that the ranking
+should be optimized for.}
 }
 \value{
 The preset to use with \code{\link[=analyze]{analyze()}}.
@@ -47,4 +51,11 @@ telomere across species.
 position data.
 \item \code{proximity} Mean proximity to telomeres.
 }
+
+Available optimization targets are:
+\itemize{
+\item \code{mean} Mean rank of the reference genes.
+\item \code{max} First rank of the reference genes.
+\item \code{min} Last rank of the reference genes.
+}
 }