#' Server implementing the main user interface.
#' @noRd
server <- function(input, output, session) {
  ranked_data <- reactive({
    total_weight <- abs(input$cross_sample_weight) + abs(input$sd_expression)
    data <- data.table::copy(ubigen::genes)

    data[, score :=
      (input$cross_sample_weight * get(input$cross_sample_metric) +
        input$sd_expression * sd_expression_normalized) /
        total_weight]

    # Normalize scores to be between 0.0 and 1.0.
    data[, score := (score - min(score)) / (max(score) - min(score))]

    data.table::setorder(data, -score)
    data[, rank := .I]
    data[, percentile := 1 - rank / max(rank)]

    data
  })

  custom_genes <- gene_selector_server("custom_genes")

  output$overview_plot <- plotly::renderPlotly(overview_plot(
    ranked_data(),
    highlighted_genes = custom_genes()
  ))

  observeEvent(custom_genes(),
    { # nolint
      if (length(custom_genes()) > 0) {
        updateTabsetPanel(session, "results_panel", selected = "custom_genes")
      } else if (input$results_panel == "custom_genes") {
        updateTabsetPanel(session, "results_panel", selected = "top_genes")
      }
    },
    ignoreNULL = FALSE
  )

  output$custom_genes_synopsis <- renderText({
    comparison_gene_ids <- custom_genes()

    if (length(comparison_gene_ids) > 1) {
      reference <- ranked_data()[!gene %chin% comparison_gene_ids, score]
      comparison <- ranked_data()[gene %chin% comparison_gene_ids, score]

      p_value <- stats::wilcox.test(
        x = comparison,
        y = reference,
        alternative = "greater"
      )$p.value

      reference_median <- stats::median(reference)
      comparison_median <- stats::median(comparison)

      HTML(glue::glue(
        "The p-value for the alternative hypothesis that your genes have ",
        "higher scores than other genes is <b>{format(round(p_value, ",
        "digits = 4), nsmall = 4, scientific = FALSE)}</b>. This value was ",
        "computed using a Wilcoxon rank sum test. The median score of your ",
        "genes is <b>{format(round(comparison_median, digits = 2), ",
        "nsmall = 2, scientific = FALSE)}</b> compared to a median score of ",
        "<b>{format(round(reference_median, digits = 2), nsmall = 2, ",
        "scientific = FALSE)}</b> of the other genes."
      ))
    }
  })

  output$custom_genes_boxplot <- plotly::renderPlotly(
    box_plot(ranked_data(), custom_genes())
  )

  output$scores_plot <- plotly::renderPlotly(scores_plot(
    ranked_data(),
    highlighted_genes = custom_genes()
  ))

  output$selected_genes <- DT::renderDataTable({
    selected_points <- plotly::event_data("plotly_selected")

    data <- if (is.null(selected_points)) {
      ranked_data()
    } else {
      ranked_data()[rank %in% selected_points$x]
    }

    genes_table(data)
  })
}

#' Create a displayable data table from the gene results data.
#' @noRd
genes_table <- function(data) {
  data <- data[, .(
    "Gene" = glue::glue_data(
      data,
      "<a href=\"https://gtexportal.org/home/gene/{hgnc_name}\" ",
      "target=\"_blank\">{hgnc_name}</a>"
    ),
    "Rank" = rank,
    "Percentile" = percentile,
    "Score" = score,
    "Median" = median_expression,
    "Mean" = mean_expression,
    "Standard deviation" = sd_expression,
    "Expressed" = above_zero,
    "Above median" = above_median,
    "Above 95%" = above_95
  )]

  DT::datatable(
    data,
    options = list(
      buttons = list(
        list(
          extend = "copy",
          text = "Copy to clipboard"
        ),
        list(
          extend = "csv",
          text = "Download CSV"
        )
      ),
      dom = "fBrtip",
      pageLength = 100
    ),
    rownames = FALSE,
    escape = FALSE,
    selection = "none",
    extensions = "Buttons"
  ) |>
    DT::formatPercentage(
      c(
        "Percentile",
        "Score",
        "Expressed",
        "Above median",
        "Above 95%"
      ),
      digits = 2,
    ) |>
    DT::formatRound(c(
      "Median",
      "Mean",
      "Standard deviation"
    ))
}