geposanui/R/server.R

# Java script function to replace gene IDs with Ensembl gene links.
js_link <- DT::JS("function(row, data) {
    let id = data[1];
    var name = data[2];
    if (!name) name = 'Unknown';
    let url = `https://www.ensembl.org/Homo_sapiens/Gene/Summary?g=${id}`;
    $('td:eq(1)', row).html(`<a href=\"${url}\" target=\"_blank\">${name}</a>`);
}")

server <- function(input, output, session) {
    input_reactives <- input_page_server("input_page")
    preset <- input_reactives$preset
    comparison_gene_ids <- input_reactives$comparison_gene_ids

    observe({
        updateNavbarPage(
            session,
            "main_page",
            selected = "Results"
        )
    }) |> bindEvent(preset(), ignoreInit = TRUE)

    # Compute the results according to the preset.
    analysis <- reactive({
        preset <- preset()

        # Perform the analysis cached based on the preset's hash.
        analysis <- withProgress(
            message = "Analyzing genes",
            value = 0.0,
            { # nolint
                geposan::analyze(preset, function(progress) {
                    setProgress(progress)
                })
            }
        )

        analysis
    })

    # Rank the results.
    ranking <- methods_server("methods", analysis, comparison_gene_ids)

    # Add gene information to the results.
    results <- reactive({
        merge(
            ranking(),
            geposan::genes,
            by.x = "gene",
            by.y = "id",
            sort = FALSE
        )
    })

    # Apply the filters.
    results_filtered <- filters_server("filters", results)

    # Server for the detailed results panel.
    results_server("results", results_filtered)

    output$rank_plot <- plotly::renderPlotly({
        preset <- preset()
        gene_sets <- list("Reference genes" = preset$reference_gene_ids)
        comparison_gene_ids <- comparison_gene_ids()

        if (length(comparison_gene_ids) >= 1) {
            gene_sets <- c(
                gene_sets,
                list("Comparison genes" = comparison_gene_ids)
            )
        }

        geposan::plot_scores(
            ranking(),
            gene_sets = gene_sets,
            max_rank = results_filtered()[, max(rank)]
        )
    })

    output$rankings_plot <- plotly::renderPlotly({
        preset <- preset()

        rankings <- list()
        methods <- preset$methods
        all <- ranking()

        for (method in methods) {
            weights <- list()
            weights[[method$id]] <- 1.0
            rankings[[method$name]] <- geposan::ranking(all, weights)
        }

        rankings[["Combined"]] <- all

        gene_sets <- list("Reference genes" = preset$reference_gene_ids)
        comparison_gene_ids <- comparison_gene_ids()

        if (length(comparison_gene_ids) >= 1) {
            gene_sets <- c(
                gene_sets,
                list("Comparison genes" = comparison_gene_ids)
            )
        }

        geposan::plot_rankings(rankings, gene_sets)
    })

    output$boxplot <- plotly::renderPlotly({
        preset <- preset()
        gene_sets <- list("Reference genes" = preset$reference_gene_ids)
        comparison_gene_ids <- comparison_gene_ids()

        if (length(comparison_gene_ids) >= 1) {
            gene_sets <- c(
                gene_sets,
                list("Comparison genes" = comparison_gene_ids)
            )
        }

        geposan::plot_boxplot(ranking(), gene_sets)
    })

    gost <- reactive({
        withProgress(
            message = "Querying g:Profiler",
            value = 0.0,
            { # nolint
                setProgress(0.2)
                gprofiler2::gost(results_filtered()[, gene])
            }
        )
    })

    output$gost_plot <- plotly::renderPlotly({
        gprofiler2::gostplot(
            gost(),
            capped = FALSE,
            interactive = TRUE
        )
    })

    output$gost_details <- DT::renderDT({
        data <- data.table(gost()$result)
        setorder(data, p_value)

        data[, total_ratio := term_size / effective_domain_size]
        data[, query_ratio := intersection_size / query_size]

        data <- data[, .(source, term_name, total_ratio, query_ratio, p_value)]

        dt <- DT::datatable(
            data,
            rownames = FALSE,
            colnames = c(
                "Source",
                "Term",
                "Total ratio",
                "Query ratio",
                "p-value"
            ),
            style = "bootstrap",
            options = list(
                pageLength = 25
            )
        )

        dt <- DT::formatRound(dt, "p_value", digits = 4)
        dt <- DT::formatPercentage(
            dt,
            c("total_ratio", "query_ratio"),
            digits = 1
        )
    })

    output$disgenet <- DT::renderDT({
        withProgress(
            message = "Querying DisGeNET",
            value = 0.0,
            { # nolint
                setProgress(0.2)

                gene_names <- results_filtered()[, name]
                gene_names <- unique(gene_names[gene_names != ""])

                diseases <- disgenet2r::disease_enrichment(gene_names)

                data <- data.table(diseases@qresult)

                data <- data[, .(Description, Ratio, BgRatio, pvalue)]
                setorder(data, pvalue)

                dt <- DT::datatable(
                    data,
                    rownames = FALSE,
                    colnames = c(
                        "Disease",
                        "Query ratio",
                        "Total ratio",
                        "p-value"
                    ),
                    style = "bootstrap",
                    options = list(
                        pageLength = 25
                    )
                )

                dt <- DT::formatRound(dt, "pvalue", digits = 4)

                dt
            }
        )
    })
}