Initial commit

R/clusteriness.R

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+# Perform a cluster analysis.
+#
+# This function will cluster the data using `hclust` and `cutree` (with the
+# specified height). Every cluster with at least two members qualifies for
+# further analysis. Clusters are then ranked based on their size in relation
+# to the number of values. The return value is a final score between zero and
+# one. Lower ranking clusters contribute less to this score.
+clusteriness_priv <- function(data, height = 1000000) {
+    n <- length(data)
+
+    # Return a score of 0.0 if there is just one or no value at all.
+    if (n < 2) {
+        return(0.0)
+    }
+
+    # Cluster the data and compute the cluster sizes.
+
+    tree <- stats::hclust(stats::dist(data))
+    clusters <- stats::cutree(tree, h = height)
+    cluster_sizes <- sort(tabulate(clusters), decreasing = TRUE)
+
+    # Compute the "clusteriness" score.
+
+    score <- 0.0
+
+    for (i in seq_along(cluster_sizes)) {
+        cluster_size <- cluster_sizes[i]
+
+        if (cluster_size >= 2) {
+            cluster_score <- cluster_size / n
+            score <- score + cluster_score / i
+        }
+    }
+
+    score
+}
+
+# Process genes clustering their distance to telomeres.
+clusteriness <- function(distances, preset) {
+    results <- data.table(gene = preset$gene_ids)
+
+    # Prefilter the input data by species.
+    distances <- distances[species %chin% preset$species_ids]
+
+    # Add an index for quickly accessing data per gene.
+    setkey(distances, gene)
+
+    # Perform the cluster analysis for one gene.
+    compute <- function(gene_id) {
+        clusteriness_priv(distances[gene_id, distance])
+    }
+
+    results[, score := compute(gene), by = 1:nrow(results)]
+}