Add new correlation method

clustering.R

@@ -1,21 +1,30 @@
 library(data.table)
 library(rlog)
 
-#' Process genes screening for a likely TPE-OLD.
+#' Process genes clustering their distance to telomeres.
 #'
-#' The return value will be a table containing genes and data to take in
-#' account when regarding them as TPE-OLD candidates.
+#' 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_input <- function(distances, species_ids, gene_ids) {
+process_clustering <- function(distances, species_ids, gene_ids) {
     results <- data.table(gene = gene_ids)
     gene_count <- length(gene_ids)
 
-    for (i in seq_along(gene_ids)) {
+    for (i in 1:gene_count) {
         gene_id <- gene_ids[i]
-        log_info(sprintf("Processing gene %i/%i (%s)", i, gene_count, gene_id))
+
+        log_info(sprintf(
+            "[%3i%%] Processing gene \"%s\"",
+            round(i / gene_count * 100),
+            gene_id
+        ))
 
         data <- distances[
             species %chin% species_ids & gene == gene_id,

correlation.R

@@ -0,0 +1,61 @@
+library(data.table)
+library(rlog)
+
+#' Compute the mean correlation coefficient comparing gene distances with a set
+#' of reference genes.
+#'
+#' The result will be a data.table with the following columns:
+#'
+#'  - `gene` Gene ID of the processed gene.
+#'  - `r_mean` Mean correlation coefficient.
+#'
+#' @param distances Distance data to use.
+#' @param species_ids Species, whose data should be included.
+#' @param gene_ids Genes to process.
+#' @param reference_gene_ids Genes to compare to.
+process_correlation <- function(distances, species_ids, gene_ids,
+                                reference_gene_ids) {
+    log_info("Processing genes for correlation")
+
+    results <- data.table(gene = gene_ids)
+    gene_count <- length(gene_ids)
+    reference_count <- length(reference_gene_ids)
+
+    # Prefilter distances by species.
+    distances <- distances[species %chin% species_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
+        ))
+
+        gene_distances <- distances[gene == gene_id]
+
+        if (nrow(gene_distances) < 12) {
+            next
+        }
+
+        #' Buffer for the sum of correlation coefficients.
+        r_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,
+                distances[gene == reference_gene_id],
+                by = "species"
+            )
+
+            r_sum <- r_sum + abs(cor(data[, distance.x], data[, distance.y]))
+        }
+
+        results[gene == gene_id, r_mean := r_sum / reference_count]
+    }
+
+    results
+}

init.R

@@ -0,0 +1,97 @@
+source("clustering.R")
+source("correlation.R")
+source("input.R")
+source("util.R")
+
+# Load input data
+
+species <- run_cached("input/species", retrieve_species)
+genes <- run_cached("input/genes", retrieve_genes)
+
+distances <- run_cached(
+    "input/distances",
+    retrieve_distances,
+    species[, id],
+    genes[, id]
+)
+
+# Load processed data
+
+all_species <- species[, id]
+replicative_species <- species[replicative == TRUE, id]
+all_genes <- genes[, id]
+tpe_old_genes <- genes[suggested | verified == TRUE, id]
+
+clustering_all <- run_cached(
+    "all_species/clustering",
+    process_clustering,
+    distances,
+    all_species,
+    all_genes
+)
+
+clustering_replicative <- run_cached(
+    "replicative_species/clustering",
+    process_clustering,
+    distances,
+    replicative_species,
+    all_genes
+)
+
+correlation_all <- run_cached(
+    "all_species/correlation",
+    process_correlation,
+    distances,
+    all_species,
+    all_genes,
+    tpe_old_genes
+)
+
+correlation_replicative <- run_cached(
+    "replicative_species/correlation",
+    process_correlation,
+    distances,
+    replicative_species,
+    all_genes,
+    tpe_old_genes
+)
+
+# Merge processed data as well as gene information.
+
+results_all <- merge(
+    genes,
+    clustering_all,
+    by.x = "id",
+    by.y = "gene"
+)
+
+results_all <- merge(
+    results_all,
+    correlation_all,
+    by.x = "id",
+    by.y = "gene"
+)
+
+results_replicative <- merge(
+    genes,
+    clustering_replicative,
+    by.x = "id",
+    by.y = "gene"
+)
+
+results_replicative <- merge(
+    results_replicative,
+    correlation_replicative,
+    by.x = "id",
+    by.y = "gene"
+)
+
+# Rename `id` columns to `gene`.
+
+setnames(results_all, "id", "gene")
+setnames(results_replicative, "id", "gene")
+
+# Order results by cluster length descendingly as a start.
+
+setorder(results_all, -cluster_length)
+setorder(results_replicative, -cluster_length)

server.R

@@ -2,61 +2,8 @@ library(data.table)
 library(DT)
 library(shiny)
 
-source("input.R")
-source("process.R")
+source("init.R")
 source("scatter_plot.R")
-source("util.R")
-
-# Load input data
-
-species <- run_cached("species", retrieve_species)
-genes <- run_cached("genes", retrieve_genes)
-
-distances <- run_cached(
-    "distances",
-    retrieve_distances,
-    species[, id],
-    genes[, id]
-)
-
-#' Results computed for all species.
-results_all <- run_cached(
-    "results_all",
-    process_input,
-    distances,
-    species[, id],
-    genes[, id]
-)
-
-#' Results computed for known replicatively aging species.
-results_replicative <- run_cached(
-    "results_replicative",
-    process_input,
-    distances,
-    species[replicative == TRUE, id],
-    genes[, id]
-)
-
-# Add gene information to results for display.
-
-results_all <- merge(
-    results_all,
-    genes,
-    by.x = "gene",
-    by.y = "id"
-)
-
-results_replicative <- merge(
-    results_replicative,
-    genes,
-    by.x = "gene",
-    by.y = "id"
-)
-
-# Order results by cluster length descendingly.
-# TODO: Once other methods have been added, this has to be dynamic.
-setorder(results_all, -cluster_length)
-setorder(results_replicative, -cluster_length)
 
 server <- function(input, output) {
     #' This expression applies all user defined filters to the available
@@ -77,14 +24,13 @@ server <- function(input, output) {
 
     output$genes <- renderDT({
         datatable(
-            results()[, .(.I, name, chromosome, cluster_length, cluster_mean)],
+            results()[, .(.I, name, cluster_length, r_mean)],
             rownames = FALSE,
             colnames = c(
                 "Rank",
                 "Gene",
-                "Chromosome",
                 "Cluster length",
-                "Cluster mean"
+                "Correlation"
             ),
             style = "bootstrap"
         )