Move species count to analysis

R/analyze.R

@@ -75,13 +75,23 @@ analyze <- function(preset, progress = NULL) {
             total_progress <- total_progress + 1 / method_count
         }
 
-        if (!is.null(progress)) {
+        # Count included species from the preset per gene.
-            progress(1.0)
+        genes_n_species <- geposan::distances[
-        }
+            species %chin% preset$species_ids,
+            .(n_species = .N),
+            by = "gene"
+        ]
 
-        results
+        setkey(genes_n_species, "gene")
+
+        # Return the results for genes with enough species.
+        results[genes_n_species[gene, n_species] >= preset$min_n_species]
     })
 
+    if (!is.null(progress)) {
+        progress(1.0)
+    }
+
     structure(
         list(
             preset = preset,

R/preset.R

@@ -14,6 +14,8 @@
 #' @param methods Methods to apply.
 #' @param species_ids IDs of species to include.
 #' @param gene_ids IDs of genes to screen.
+#' @param min_n_species Minimum number of orthologs that a gene should have to
+#'   be included in the analysis.
 #' @param reference_gene_ids IDs of reference genes to compare to.
 #'
 #' @return The preset to use with [analyze()].
@@ -27,6 +29,7 @@ preset <- function(methods = c(
                    ),
                    species_ids = NULL,
                    gene_ids = NULL,
+                   min_n_species = 10,
                    reference_gene_ids = NULL) {
     # The included data gets sorted to be able to produce predictable hashes
     # for the object later.
@@ -35,6 +38,7 @@ preset <- function(methods = c(
             methods = sort(methods),
             species_ids = sort(species_ids),
             gene_ids = sort(gene_ids),
+            min_n_species = as.numeric(min_n_species),
             reference_gene_ids = sort(reference_gene_ids)
         ),
         class = "geposan_preset"
@@ -60,6 +64,8 @@ print.geposan_preset <- function(x, ...) {
         length(x$gene_ids)
     ))
 
+    cat(sprintf("\n  Species per gene: \u2265 %i", x$min_n_species))
+
     cat(sprintf(
         "\n  Comparison data: %i reference genes\n",
         length(x$reference_gene_ids)

R/ranking.R

@@ -6,33 +6,18 @@
 #' @param analysis Analysis object resulting from [analyze()].
 #' @param weights Named list pairing method names with weighting factors. Only
 #'   methods that are contained within this list will be included.
-#' @param min_n_species Minimum number of required species per gene. Genes that
-#'   have fewer species will not be included in the ranking.
 #'
 #' @returns A ranking object. The object extends the analysis result with
 #'   additional columns containing the `score` and the `rank` of each gene. It
 #'   will be ordered by rank.
 #'
 #' @export
-ranking <- function(analysis, weights, min_n_species = 10) {
+ranking <- function(analysis, weights) {
     if (!"geposan_analysis" %chin% class(analysis)) {
         stop("Invalid analyis. Use geposan::analyze().")
     }
 
-    # Count included species from the preset per gene.
+    ranking <- copy(analysis$results)
-    genes_n_species <- geposan::distances[
-        species %chin% analysis$preset$species_ids,
-        .(n_species = .N),
-        by = "gene"
-    ]
-
-    setkey(genes_n_species, gene)
-
-    # Exclude genes with too few species.
-    ranking <- analysis$results[
-        genes_n_species[gene, n_species] >= min_n_species
-    ]
-
     ranking[, score := 0.0]
 
     for (method in names(weights)) {
@@ -65,16 +50,13 @@ ranking <- function(analysis, weights, min_n_species = 10) {
 #' @param reference_gene_ids IDs of the reference genes.
 #' @param target The optimization target. It may be one of "mean", "min" or
 #'   "max" and results in the respective rank being optimized.
-#' @param min_n_species Minimum number of required species per gene. Genes that
-#'   have fewer species will not be included in the rankings used to find the
-#'   optimal weights.
 #'
 #' @returns Named list pairing method names with their optimal weights. This
 #'   can be used as an argument to [ranking()].
 #'
 #' @export
 optimal_weights <- function(analysis, methods, reference_gene_ids,
-                            target = "mean", min_n_species = 10) {
+                            target = "mean") {
     if (!"geposan_analysis" %chin% class(analysis)) {
         stop("Invalid analyis. Use geposan::analyze().")
     }
@@ -92,11 +74,7 @@ optimal_weights <- function(analysis, methods, reference_gene_ids,
 
     # Compute the target rank of the reference genes when applying the weights.
     target_rank <- function(factors) {
-        data <- ranking(
+        data <- ranking(analysis, weights(factors))
-            analysis,
-            weights(factors),
-            min_n_species = min_n_species
-        )
 
         result <- data[gene %chin% reference_gene_ids, if (target == "min") {
             min(rank)

man/optimal_weights.Rd

@@ -4,13 +4,7 @@
 \alias{optimal_weights}
 \title{Find the best weights to rank the results.}
 \usage{
-optimal_weights(
+optimal_weights(analysis, methods, reference_gene_ids, target = "mean")
-  analysis,
-  methods,
-  reference_gene_ids,
-  target = "mean",
-  min_n_species = 10
-)
 }
 \arguments{
 \item{analysis}{Results from \code{\link[=analyze]{analyze()}} or \code{\link[=ranking]{ranking()}}.}
@@ -21,10 +15,6 @@ optimal_weights(
 
 \item{target}{The optimization target. It may be one of "mean", "min" or
 "max" and results in the respective rank being optimized.}
-
-\item{min_n_species}{Minimum number of required species per gene. Genes that
-have fewer species will not be included in the rankings used to find the
-optimal weights.}
 }
 \value{
 Named list pairing method names with their optimal weights. This

man/preset.Rd

@@ -8,6 +8,7 @@ preset(
   methods = c("clusteriness", "correlation", "neural", "proximity"),
   species_ids = NULL,
   gene_ids = NULL,
+  min_n_species = 10,
   reference_gene_ids = NULL
 )
 }
@@ -18,6 +19,9 @@ preset(
 
 \item{gene_ids}{IDs of genes to screen.}
 
+\item{min_n_species}{Minimum number of orthologs that a gene should have to
+be included in the analysis.}
+
 \item{reference_gene_ids}{IDs of reference genes to compare to.}
 }
 \value{

man/ranking.Rd

@@ -4,16 +4,13 @@
 \alias{ranking}
 \title{Rank the results by computing a score.}
 \usage{
-ranking(analysis, weights, min_n_species = 10)
+ranking(analysis, weights)
 }
 \arguments{
 \item{analysis}{Analysis object resulting from \code{\link[=analyze]{analyze()}}.}
 
 \item{weights}{Named list pairing method names with weighting factors. Only
 methods that are contained within this list will be included.}
-
-\item{min_n_species}{Minimum number of required species per gene. Genes that
-have fewer species will not be included in the ranking.}
 }
 \value{
 A ranking object. The object extends the analysis result with