Add initial gene processing

input.R

@@ -15,6 +15,7 @@ load_input <- function(path) {
 
     species <- data.table(
         id = character(),
+        group = character(),
         label = character(),
         median_distance = numeric()
     )
@@ -44,6 +45,7 @@ load_input <- function(path) {
         # add it to the species table along other static data.
         species <- rbindlist(list(species, data.table(
             id = species_id,
+            group = original_species[id == species_id, group],
             label = original_species[id == species_id, label],
             median_distance = median(species_distances[, dist])
         )))

process.R

@@ -0,0 +1,51 @@
+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 input Data from [`load_input()`].
+process_input <- function(input) {
+    results <- data.table(gene = input$genes$id)
+
+    # Exclude species with naturally or artificially short chromosomes as well
+    # as non-replicatively aging species.
+    species_ids <- input$species[
+        median_distance >= 7500000 & group == "replicative",
+        id
+    ]
+
+    gene_ids <- input$genes[, id]
+    gene_count <- length(gene_ids)
+
+    for (i in seq_along(gene_ids)) {
+        gene_id <- gene_ids[i]
+        log_info(sprintf("Processing gene %i/%i (%i)", i, gene_count, gene_id))
+
+        distances <- input$distances[
+            species %chin% species_ids & gene == gene_id,
+            .(species, distance)
+        ]
+
+        if (distances[, .N] < 12) {
+            next
+        }
+
+        clusters <- hclust(dist(distances[, distance]))
+        clusters_cut <- cutree(clusters, h = 1000000)
+        cluster <- distances[which(clusters_cut == 1)]
+
+        results[
+            gene == gene_id,
+            `:=`(
+                cluster_length = cluster[, .N],
+                cluster_mean = mean(cluster[, distance]),
+                cluster_species = list(cluster[, species])
+            )
+        ]
+    }
+
+    results
+}

server.R

@@ -3,22 +3,35 @@ library(DT)
 library(shiny)
 
 source("input.R")
+source("process.R")
 source("scatter_plot.R")
 source("util.R")
 
 data <- run_cached("input", load_input, "input")
+results <- run_cached("results", process_input, data)
 
 server <- function(input, output) {
+    filtered <- results[cluster_length >= 10]
+    merged <- merge.data.table(filtered, data$genes, by.x = "gene", by.y = "id")
+    setorder(merged, -cluster_length)
+
     output$genes <- renderDT({
         datatable(
-            data$genes[, c("name", "chromosome")],
+            merged[, .(.I, name, chromosome, cluster_length, cluster_mean)],
             rownames = FALSE,
+            colnames = c(
+                "Rank",
+                "Gene",
+                "Chromosome",
+                "Cluster length",
+                "Cluster mean"
+            ),
             style = "bootstrap"
         )
     })
 
     output$scatter <- renderPlot({
-        gene_ids <- data$genes[input$genes_rows_selected, id]
+        gene_ids <- merged[input$genes_rows_selected, gene]
         scatter_plot(gene_ids, data)
     })
 }