Initial commit

R/correlation.R

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+# Compute the mean correlation coefficient comparing gene distances with a set
+# of reference genes.
+correlation <- function(distances, preset) {
+    results <- data.table(gene = preset$gene_ids)
+    reference_gene_ids <- preset$reference_gene_ids
+    reference_count <- length(reference_gene_ids)
+
+    # Prefilter distances by species.
+    distances <- distances[species %chin% preset$species_ids]
+
+    # Add an index for quickly accessing data per gene.
+    setkey(distances, gene)
+
+    # Prepare the reference genes' data.
+    reference_distances <- distances[gene %chin% reference_gene_ids]
+
+    # Perform the correlation for one gene.
+    compute <- function(gene_id) {
+        gene_distances <- distances[gene_id]
+        gene_species_count <- nrow(gene_distances)
+
+        # Return a score of 0.0 if there is just one or no value at all.
+        if (gene_species_count <= 1) {
+            return(0.0)
+        }
+
+        # Buffer for the sum of correlation coefficients.
+        correlation_sum <- 0
+
+        # Correlate with all reference genes but not with the gene itself.
+        for (reference_gene_id in
+             reference_gene_ids[reference_gene_ids != gene_id]) {
+            data <- merge(
+                gene_distances,
+                reference_distances[reference_gene_id],
+                by = "species"
+            )
+
+            # Skip this reference gene, if there are not enough value pairs.
+            # This will lessen the final score, because it effectively
+            # represents a correlation coefficient of 0.0.
+            if (nrow(data) <= 1) {
+                next
+            }
+
+            # Order data by the reference gene's distance to get a monotonic
+            # relation.
+            setorder(data, distance.y)
+
+            correlation_sum <- correlation_sum + abs(stats::cor(
+                data[, distance.x], data[, distance.y],
+                method = "spearman"
+            ))
+        }
+
+        # Compute the score as the mean correlation coefficient.
+        score <- correlation_sum / reference_count
+    }
+
+    results[, score := compute(gene), by = 1:nrow(results)]
+}