Reorganize source files and generalize presets

process/input.R

@@ -0,0 +1,254 @@
+library(biomaRt)
+library(data.table)
+library(progress)
+library(rlog)
+library(stringr)
+
+#' Species IDs of known replicatively aging species.
+species_ids_replicative <- c(
+    "bihybrid",
+    "btaurus",
+    "bthybrid",
+    "cfamiliaris",
+    "chircus",
+    "cjacchus",
+    "clfamiliaris",
+    "csabaeus",
+    "ecaballus",
+    "fcatus",
+    "ggorilla",
+    "hsapiens",
+    "lafricana",
+    "mfascicularis",
+    "mmulatta",
+    "mmurinus",
+    "mnemestrina",
+    "nleucogenys",
+    "oaries",
+    "pabelii",
+    "panubis",
+    "ppaniscus",
+    "ptroglodytes",
+    "sscrofa",
+    "tgelada"
+)
+
+#' Gene names of genes for verified TPE-OLD genes.
+genes_verified_tpe_old <- c(
+    "C1S",
+    "DSP",
+    "ISG15",
+    "SORBS2",
+    "TERT"
+)
+
+#' Gene names of genes with a suggested TPE-OLD.
+genes_suggested_tpe_old <- c(
+    "AKAP3",
+    "ANO2",
+    "CCND2",
+    "CD163L1",
+    "CD9",
+    "FOXM1",
+    "GALNT8",
+    "NDUFA9",
+    "TEAD4",
+    "TIGAR",
+    "TSPAN9"
+)
+
+#' Shared accessor for the Ensembl API.
+ensembl <- NULL
+
+#' Get the ensembl accessor and initialize it if necessary.
+get_ensembl <- function() {
+    if (is.null(ensembl)) {
+        ensembl <<- useEnsembl("ensembl", version = 104)
+    }
+
+    ensembl
+}
+
+#' Get all chromosome names for a Ensembl dataset.
+#'
+#' Valid chromosome names include decimal numbers as well as 'X' and 'Y'.
+get_chromosome_names <- function(dataset) {
+    chromosome_names <- listFilterOptions(dataset, "chromosome_name")
+    chromosome_names[str_which(chromosome_names, "^[0-9]+|[XY]$")]
+}
+
+#' Retrieve information on species.
+#'
+#' The result will be a `data.table` with the following columns:
+#'
+#'  - `id` Species ID as presented by Ensembl.
+#'  - `name` Human readable species name.
+#'  - `replicative` Whether the species is likely to be aging replicatively.
+retrieve_species <- function() {
+    # Ensembl datasets correspond to distinct species.
+    ensembl_datasets <- data.table(listDatasets(get_ensembl()))
+
+    # Filter out species ID and name from the result.
+    species <- ensembl_datasets[, .(
+        id = str_match(dataset, "(.*)_gene_ensembl")[, 2],
+        name = str_match(description, "(.*) genes \\(.*\\)")[, 2]
+    )]
+
+    species[, replicative := id %chin% species_ids_replicative]
+}
+
+#' Retrieve information on human genes.
+#'
+#' This will only include genes on assembled chromosomes. Chromosomes are
+#' filtered based on their name being either a decimal number, 'X' or 'Y'.
+#'
+#' The result will be a `data.table` with the following columns:
+#'
+#'  - `id` Ensembl gene ID.
+#'  - `ǹame` HGNC name of the gene.
+#'  - `chromosome` Human chromosome on which the gene is located.
+retrieve_genes <- function() {
+    dataset <- useDataset("hsapiens_gene_ensembl", mart = get_ensembl())
+
+    genes <- data.table(getBM(
+        attributes = c("ensembl_gene_id", "hgnc_symbol", "chromosome_name"),
+        filters = "chromosome_name",
+        values = get_chromosome_names(dataset),
+        mart = useDataset("hsapiens_gene_ensembl", mart = get_ensembl())
+    ))
+
+    genes[, .(
+        id = ensembl_gene_id,
+        name = hgnc_symbol,
+        chromosome = chromosome_name,
+        verified = hgnc_symbol %chin% genes_verified_tpe_old,
+        suggested = hgnc_symbol %chin% genes_suggested_tpe_old
+    )]
+}
+
+#' Retrieve gene distance data.
+#'
+#' The data will include all available values for the given species and genes
+#' that are located on assembled chromosomes.
+#'
+#' The result will be a `data.table` with the following columns:
+#'
+#'  - `species` Species ID.
+#'  - `gene` Ensembl gene ID.
+#'  - `distance` Distance to nearest telomere in base pairs.
+retrieve_distances <- function(species_ids, gene_ids) {
+    ensembl <- get_ensembl()
+
+    # Exclude the human from the species, in case it is present there.
+    species_ids <- species_ids[species_ids != "hsapiens"]
+
+    species_count <- length(species_ids)
+    gene_count <- length(gene_ids)
+
+    log_info(sprintf(
+        "Retrieving distance data for %i genes from %i species",
+        gene_count,
+        species_count
+    ))
+
+    progress <- progress_bar$new(
+        total = gene_count,
+        format = "Retrieving distance data [:bar] :percent (ETA :eta)"
+    )
+
+    # Special case the human species and retrieve all available distance
+    # information.
+
+    dataset <- useDataset("hsapiens_gene_ensembl", mart = ensembl)
+
+    human_distances <- data.table(getBM(
+        attributes = c(
+            "ensembl_gene_id",
+            "chromosome_name",
+            "start_position",
+            "end_position"
+        ),
+        filters = "chromosome_name",
+        values = get_chromosome_names(dataset),
+        mart = dataset
+    ))
+
+    # Compute the nearest distance to telomeres.
+
+    human_distances[,
+        chromosome_length := max(end_position),
+        by = chromosome_name
+    ]
+
+    distances <- human_distances[, .(
+        species = "hsapiens",
+        gene = ensembl_gene_id,
+        distance = pmin(
+            start_position,
+            chromosome_length - end_position
+        )
+    )]
+
+    for (i in 1:species_count) {
+        species_id <- species_ids[i]
+
+        progress$tick()
+
+        dataset <- useDataset(
+            sprintf("%s_gene_ensembl", species_id),
+            mart = ensembl
+        )
+
+        # Besides the attributes that are always present, we need to check for
+        # human orthologs. Some species don't have that information and will be
+        # skipped.
+        if (!"hsapiens_homolog_ensembl_gene" %chin%
+            listAttributes(dataset, what = "name")) {
+            next
+        }
+
+        chromosome_names <- get_chromosome_names(dataset)
+
+        # Skip the species, if there are no assembled chromosomes.
+        if (length(chromosome_names) <= 0) {
+            next
+        }
+
+        # Retrieve information on all genes of the current species, that have
+        # human orthologs. This is called "homolog" in the Ensembl schema.
+        ensembl_distances <- data.table(getBM(
+            attributes = c(
+                "hsapiens_homolog_ensembl_gene",
+                "chromosome_name",
+                "start_position",
+                "end_position"
+            ),
+            filters = c("with_hsapiens_homolog", "chromosome_name"),
+            values = list(TRUE, chromosome_names),
+            mart = dataset
+        ))
+
+        # Precompute the genes' distance to the nearest telomere.
+
+        ensembl_distances[,
+            chromosome_length := max(end_position),
+            by = chromosome_name
+        ]
+
+        species_distances <- ensembl_distances[, .(
+            species = species_id,
+            gene = hsapiens_homolog_ensembl_gene,
+            distance = pmin(
+                start_position,
+                chromosome_length - end_position
+            )
+        )]
+
+        distances <- rbindlist(list(distances, species_distances))
+    }
+
+    # Arbitrarily exclude duplicated genes.
+    # TODO: Consider a refined approach or work out how to include all
+    # duplicates.
+    unique(distances, by = c("species", "gene"))
+}