Add new correlation method

process.R

@@ -1,51 +0,0 @@
-library(data.table)
-library(rlog)
-
-#' Process genes screening for a likely TPE-OLD.
-#'
-#' The return value will be a table containing genes and data to take in
-#' account when regarding them as TPE-OLD candidates.
-#'
-#' @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_input <- function(distances, species_ids, gene_ids) {
-    results <- data.table(gene = gene_ids)
-    gene_count <- length(gene_ids)
-
-    for (i in seq_along(gene_ids)) {
-        gene_id <- gene_ids[i]
-        log_info(sprintf("Processing gene %i/%i (%s)", i, gene_count, 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
-}