Allow selecting the expression dataset

R/data.R

@@ -1,2 +1,9 @@
-#' A `data.table` containig data on genes and their expression behavior.
+#' A `data.table` containig mappings of Ensembl gene ID to HGNC symbol.
 "genes"
+
+#' The results from the analysis across all GTEx samples.
+"gtex_all"
+
+#' The results from the analysis across Human Protein Atlas' tissue aggregated
+#' data.
+"hpa_tissues"

R/ranking.R

@@ -10,7 +10,7 @@
 #'   percentiles for each gene.
 #'
 #' @export
-rank_genes <- function(data = ubigen::genes,
+rank_genes <- function(data = ubigen::gtex_all,
                        cross_sample_metric = "above_95",
                        cross_sample_weight = 0.5,
                        level_metric = "median_expression_normalized",

R/server.R

@@ -1,8 +1,19 @@
 #' Server implementing the main user interface.
 #' @noRd
 server <- function(input, output, session) {
+  dataset <- reactive({
+    analysis <- if (input$dataset == "hpa_tissues") {
+      ubigen::hpa_tissues
+    } else {
+      ubigen::gtex_all
+    }
+
+    merge(analysis, ubigen::genes, by = "gene")
+  })
+
   ranked_data <- reactive({
     rank_genes(
+      data = dataset(),
       cross_sample_metric = input$cross_sample_metric,
       cross_sample_weight = input$cross_sample_weight,
       level_metric = input$level_metric,

R/ui.R

@@ -19,6 +19,14 @@ ui <- function() {
             h3("Your genes"),
             gene_selector_ui("custom_genes"),
             h3("Method"),
+            selectInput(
+              "dataset",
+              label = strong("Expression dataset"),
+              list(
+                "GTEx (all samples)" = "gtex_all",
+                "Human Protein Atlas (tissues)" = "hpa_tissues"
+              )
+            ),
             selectInput(
               "cross_sample_metric",
               verticalLayout(

man/genes.Rd

@@ -3,14 +3,14 @@
 \docType{data}
 \name{genes}
 \alias{genes}
-\title{A \code{data.table} containig data on genes and their expression behavior.}
+\title{A \code{data.table} containig mappings of Ensembl gene ID to HGNC symbol.}
 \format{
-An object of class \code{data.table} (inherits from \code{data.frame}) with 55507 rows and 20 columns.
+An object of class \code{data.table} (inherits from \code{data.frame}) with 55507 rows and 2 columns.
 }
 \usage{
 genes
 }
 \description{
-A \code{data.table} containig data on genes and their expression behavior.
+A \code{data.table} containig mappings of Ensembl gene ID to HGNC symbol.
 }
 \keyword{datasets}

man/gtex_all.Rd

@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{gtex_all}
+\alias{gtex_all}
+\title{The results from the analysis across all GTEx samples.}
+\format{
+An object of class \code{data.table} (inherits from \code{data.frame}) with 55507 rows and 17 columns.
+}
+\usage{
+gtex_all
+}
+\description{
+The results from the analysis across all GTEx samples.
+}
+\keyword{datasets}

man/hpa_tissues.Rd

@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{hpa_tissues}
+\alias{hpa_tissues}
+\title{The results from the analysis across Human Protein Atlas' tissue aggregated
+data.}
+\format{
+An object of class \code{data.table} (inherits from \code{data.frame}) with 20090 rows and 17 columns.
+}
+\usage{
+hpa_tissues
+}
+\description{
+The results from the analysis across Human Protein Atlas' tissue aggregated
+data.
+}
+\keyword{datasets}

man/rank_genes.Rd

@@ -5,7 +5,7 @@
 \title{Rank genes based on how ubiquitous they are.}
 \usage{
 rank_genes(
-  data = ubigen::genes,
+  data = ubigen::gtex_all,
   cross_sample_metric = "above_95",
   cross_sample_weight = 0.5,
   level_metric = "median_expression_normalized",

scripts/analyze.R

@@ -1,12 +0,0 @@
-# This scripts reads the input data (See input.R) and performs various
-# computations on it in order to later use the results for computating scores
-# for ubuiquitously expressed genes.
-
-library(data.table)
-library(here)
-
-i_am("scripts/input.R")
-
-data <- fread(here("scripts", "input", "data_long.csv"))
-results <- ubigen::analyze(data)
-fwrite(results, file = here("scripts", "output", "results.csv"))

scripts/gtex_all.R

@@ -0,0 +1,32 @@
+# This script uses the results (See results.csv) and computes a score for each
+# gene. This is the data that will be used in the package.
+
+library(data.table)
+library(here)
+
+i_am("scripts/input.R")
+
+data <- fread(here("scripts", "input", "data_long.csv"))
+gtex_all <- ubigen::analyze(data)
+
+# To save memory, the data includes fake IDs for genes. The actual Ensembl IDs
+# are part of the separate genes table.
+
+genes <- fread(here("scripts", "input", "genes.csv"))
+
+setnames(gtex_all, "gene", "id")
+
+data <- merge(
+  gtex_all,
+  genes[, .(id, gene)],
+  by = "id",
+  all.x = TRUE,
+  sort = FALSE
+)
+
+data[, id := NULL]
+
+usethis::use_data(gtex_all, overwrite = TRUE)
+
+genes[, id := NULL]
+usethis::use_data(genes, overwrite = TRUE)

scripts/hpa.R

@@ -0,0 +1,12 @@
+library(data.table)
+library(here)
+
+i_am("scripts/hpa.R")
+
+# Source: https://www.proteinatlas.org/download/rna_tissue_hpa.tsv.zip
+data <- fread(here("scripts", "input", "rna_tissue_hpa.tsv"))
+setnames(data, c("Gene", "Tissue", "nTPM"), c("gene", "sample", "expression"))
+data[, `:=`("Gene name" = NULL, TPM = NULL, pTPM = NULL)]
+
+hpa_tissues <- ubigen::analyze(data)
+usethis::use_data(hpa_tissues, overwrite = TRUE)

scripts/ranking.R

@@ -1,39 +0,0 @@
-# This script uses the results (See results.csv) and computes a score for each
-# gene. This is the data that will be used in the package.
-
-library(data.table)
-library(here)
-
-i_am("scripts/input.R")
-
-# To save memory, the data includes fake IDs for genes. The actual Ensembl IDs
-# are part of the separate genes table.
-
-genes <- fread(here("scripts", "input", "genes.csv"))
-data <- fread(here("scripts", "output", "results.csv"))
-
-# Rank the data using default parameters.
-data <- ubigen::rank_genes(data = data)
-
-# Reintroduce gene IDs and HGNC symbols.
-
-setnames(data, "gene", "id")
-
-data <- merge(
-  data,
-  genes,
-  by = "id",
-  all.x = TRUE,
-  sort = FALSE
-)
-
-setnames(data, "hgnc_symbol", "hgnc_name")
-data[, id := NULL]
-
-# Remove duplicates. This will keep the best row for each duplicated gene.
-data <- unique(data, by = "gene")
-
-# Reassign ranks, because duplicates may have been removed.
-data[, rank := .I]
-
-fwrite(data, file = here("scripts", "output", "genes.csv"))