Add new correlation method

clustering.R

@@ -0,0 +1,60 @@
+library(data.table)
+library(rlog)
+
+#' Process genes clustering their distance to telomeres.
+#'
+#' The return value will be a data.table with the following columns:
+#'
+#'  - `gene` Gene ID of the processed gene.
+#'  - `cluster_length` Length of the largest cluster.
+#'  - `cluster_mean` Mean value of the largest cluster.
+#'  - `cluster_species` List of species contributing to the largest cluster.
+#'
+#' @param distances Gene distance data to use.
+#' @param species_ids IDs of species to include in the analysis.
+#' @param gene_ids Genes to include in the computation.
+process_clustering <- function(distances, species_ids, gene_ids) {
+    results <- data.table(gene = gene_ids)
+    gene_count <- length(gene_ids)
+
+    for (i in 1:gene_count) {
+        gene_id <- gene_ids[i]
+
+        log_info(sprintf(
+            "[%3i%%] Processing gene \"%s\"",
+            round(i / gene_count * 100),
+            gene_id
+        ))
+
+        data <- distances[
+            species %chin% species_ids & gene == gene_id,
+            .(species, distance)
+        ]
+
+        if (data[, .N] < 12) {
+            next
+        }
+
+        clusters <- hclust(dist(data[, distance]))
+        clusters_cut <- cutree(clusters, h = 1000000)
+
+        # Find the largest cluster
+        cluster_indices <- unique(clusters_cut)
+        cluster_index <- cluster_indices[
+            which.max(tabulate(match(clusters_cut, cluster_indices)))
+        ]
+
+        cluster <- data[which(clusters_cut == cluster_index)]
+
+        results[
+            gene == gene_id,
+            `:=`(
+                cluster_length = cluster[, .N],
+                cluster_mean = mean(cluster[, distance]),
+                cluster_species = list(cluster[, species])
+            )
+        ]
+    }
+
+    results
+}