Store more method results

R/analyze.R

@@ -52,7 +52,7 @@ analyze <- function(preset, progress = NULL) {
             method_results <- methods[[method_id]](
                 preset,
                 progress = method_progress
-            )
+            )$results
 
             setnames(method_results, "score", method_id)
 

R/clusteriness.R

@@ -65,6 +65,14 @@ clusteriness <- function(preset, progress = NULL) {
             score
         }
 
-        results[, score := compute(gene), by = 1:nrow(results)]
+        structure(
+            list(
+                results = results[,
+                    score := compute(gene),
+                    by = gene
+                ]
+            ),
+            class = "geposan_method_results"
+        )
     })
 }

R/correlation.R

@@ -76,6 +76,14 @@ correlation <- function(preset, progress = NULL) {
             ]
 
             results[, .(gene, score)]
+
+            structure(
+                list(
+                    results = results[, .(gene, score)],
+                    all_correlations = results
+                ),
+                class = "geposan_method_results"
+            )
         }
     )
 }

R/neural.R

@@ -120,7 +120,7 @@ neural <- function(preset, progress = NULL, seed = 49641) {
             colnames(training_matrix) <- NULL
             training_matrix <- keras::normalize(training_matrix)
 
-            keras::fit(
+            fit <- keras::fit(
                 model,
                 x = training_matrix,
                 y = training_data$score,
@@ -142,22 +142,40 @@ neural <- function(preset, progress = NULL, seed = 49641) {
                 progress_buffer <<- progress_buffer + progress_step
                 progress(progress_buffer)
             }
+
+            list(
+                training_gene_ids = training_gene_ids,
+                gene_ids = gene_ids,
+                model = model,
+                fit = fit
+            )
         }
 
         # Apply the network to all non-training genes first.
-        apply_network(
+        network <- apply_network(
             training_data$gene,
             gene_ids[!gene_ids %chin% training_data$gene]
         )
 
+        cross_networks <- NULL
+
         # Apply the network to the training genes leaving out the gene itself.
         for (training_gene_id in training_data$gene) {
-            apply_network(
+            cross_network <- apply_network(
                 training_data[gene != training_gene_id, gene],
                 training_gene_id
             )
+
+            cross_networks <- c(cross_networks, cross_network)
         }
 
-        data[, .(gene, score)]
+        structure(
+            list(
+                results = data[, .(gene, score)],
+                network = network,
+                cross_networks = cross_networks
+            ),
+            class = "geposan_method_results"
+        )
     })
 }

R/proximity.R

@@ -24,6 +24,11 @@ proximity <- function(preset, progress = NULL) {
             progress(1.0)
         }
 
-        data[, .(gene, score)]
+        structure(
+            list(
+                results = data[, .(gene, score)]
+            ),
+            class = "geposan_method_results"
+        )
     })
 }