Adapt to geposan changes and add new methods

R/data.R

@@ -71,13 +71,23 @@ genes <- geposan::genes[, .(
 methods <- list(
     list(
         id = "clusteriness",
-        name = "Clustering",
+        name = "Clustering (distances)",
-        description = "Clustering of genes"
+        description = "Clustering of genes (distances)"
+    ),
+    list(
+        id = "clusteriness_positions",
+        name = "Clustering (positions)",
+        description = "Clustering of genes (positions)"
     ),
     list(
         id = "correlation",
-        name = "Correlation",
+        name = "Correlation (distances)",
-        description = "Correlation with known genes"
+        description = "Correlation with known genes (distances)"
+    ),
+    list(
+        id = "correlation_positions",
+        name = "Correlation (positions)",
+        description = "Correlation with known genes (positions)"
     ),
     list(
         id = "proximity",
@@ -91,6 +101,12 @@ methods <- list(
     )
 )
 
+# IDs of methods for geposan.
+method_ids <- sapply(methods, function(method) method$id)
+
+# Names of methods for geposan.
+method_names <- sapply(methods, function(method) method$name)
+
 # Gene IDs of known or suggested TPE-OLD genes.
 genes_tpe_old <- genes[suggested | verified == TRUE, id]
 
@@ -99,17 +115,17 @@ species_replicative <- species[replicative == TRUE, id]
 
 # Preset for [geposan] including all species and TPE-OLD genes for reference.
 preset_all_species <- geposan::preset(
-    methods = c("clusteriness", "correlation", "proximity", "neural"),
+    methods = method_ids,
-    species = species$id,
+    species_ids = species$id,
-    genes = genes$id,
+    gene_ids = genes$id,
-    reference_genes = genes_tpe_old
+    reference_gene_ids = genes_tpe_old
 )
 
 # Preset for [geposan] including only replicatively aging species as well as
 # TPE-OLD genes for reference.
 preset_replicative_species <- geposan::preset(
-    methods = c("clusteriness", "correlation", "proximity", "neural"),
+    methods = method_ids,
-    species = species_replicative,
+    species_ids = species_replicative,
-    genes = genes$id,
+    gene_ids = genes$id,
-    reference_genes = genes_tpe_old
+    reference_gene_ids = genes_tpe_old
 )

R/methods.R

@@ -46,9 +46,11 @@ methods_ui <- function(id) {
 # Construct server for the methods editor.
 #
 # @param analysis The reactive containing the results to be weighted.
+# @param min_n_species A reactive containing the minimum number of species to
+#   require for genes to be included in the ranking.
 #
 # @return A reactive containing the weighted results.
-methods_server <- function(id, analysis) {
+methods_server <- function(id, analysis, min_n_species) {
     moduleServer(id, function(input, output, session) {
         observeEvent(input$optimize_button, {
             method_ids <- NULL
@@ -60,11 +62,12 @@ methods_server <- function(id, analysis) {
                 }
             }
 
-            weights <- geposan::optimize_weights(
+            weights <- geposan::optimal_weights(
                 analysis(),
                 method_ids,
                 genes_tpe_old,
-                target = input$target
+                target = input$target,
+                min_n_species = min_n_species()
             )
 
             for (method_id in method_ids) {
@@ -95,7 +98,11 @@ methods_server <- function(id, analysis) {
                 }
             }
 
-            geposan::ranking(analysis(), weights)
+            geposan::ranking(
+                analysis(),
+                weights,
+                min_n_species = min_n_species()
+            )
         })
     })
 }

R/preset_editor.R

@@ -82,15 +82,10 @@ preset_editor_server <- function(id) {
 
         observeEvent(input$apply_button, {
             result(geposan::preset(
-                methods <- c(
+                methods = method_ids,
-                    "clusteriness",
+                species_ids = input$species,
-                    "correlation",
+                gene_ids = genes$id,
-                    "proximity",
+                reference_gene_ids = input$reference_genes
-                    "neural"
-                ),
-                species = input$species,
-                genes = genes$id,
-                reference_genes = input$reference_genes
             ))
         })
 

R/server.R

@@ -24,45 +24,39 @@ server <- function(input, output, session) {
         preset <- preset()
 
         # Perform the analysis cached based on the preset's hash.
-        results <- withProgress(
+        analysis <- withProgress(
             message = "Analyzing genes",
-            value = 0.0, {
+            value = 0.0,
+            { # nolint
                 geposan::analyze(preset, function(progress) {
                     setProgress(progress)
                 })
             }
         )
 
-        # Add all gene information to the results.
+        analysis
-        results <- merge(
-            results,
-            genes,
-            by.x = "gene",
-            by.y = "id"
-        )
-
-        # Count included species from the preset per gene.
-        genes_n_species <- geposan::distances[
-            species %chin% preset$species_ids,
-            .(n_species = .N),
-            by = "gene"
-        ]
-
-        setkey(genes_n_species, gene)
-
-        # Exclude genes with too few species.
-        results[genes_n_species[gene, n_species] >= input$n_species]
     })
 
+    min_n_species <- reactive(input$n_species)
+
     # Rank the results.
-    results <- methods_server("methods", analysis)
+    ranking <- methods_server("methods", analysis, min_n_species)
+
+    # Add gene information to the results.
+    results <- reactive({
+        merge(
+            ranking(),
+            geposan::genes,
+            by.x = "gene",
+            by.y = "id",
+            sort = FALSE
+        )
+    })
 
     # Apply the filters.
     results_filtered <- filters_server("filters", results)
 
     output$genes <- DT::renderDT({
-        method_ids <- sapply(methods, function(method) method$id)
-        method_names <- sapply(methods, function(method) method$name)
         columns <- c("rank", "gene", "name", "chromosome", method_ids, "score")
         column_names <- c("", "Gene", "", "Chromosome", method_names, "Score")