inst/shiny/modules/18-ui-genie3-network.R

In goushixue/QRseq: Quickly RNA-seq Analysis Platform

fluidPage(
  style = "margin-left: 10px; margin-right:10px;",
  column(
    4,
    box(
      title = "Running parameters:", id = "run_genie_card", width = 12, collapsible = TRUE, solidHeader = TRUE,
      selectInput("genie3_genes", "What genes to used:", width = "100%",
                  c("Differential Genes", "Pattern Genes", "WGCNA Module Genes")),
      uiOutput("genie3_group"),
      selectInput("g3_method", "treeMethod", choices = c("Random Forests" = "RF", "Extra-Trees" = "ET"), width = "100%"),
      selectInput("g3K", "Number of candidate regulators at nodes (K):", choices = c("sqrt", "all"), width = "100%"),
      numericInput("g3_nTrees", "nTrees:", value = 1000, width = "100%"),
      numericInput("g3_core", "Number of cores for parallel computing:", value = 1, width = "100%"),
      numericInput("g3_reportMax", "Maximum number of links to report:", value = 15000, width = "100%"),
      actionButton("run_genie", "Run GENIE3", class = "run-button", width = "100%")
    ),
    conditionalPanel(
      "input.run_genie",
      box(
        title = "Plot parameters:", width = 12, collapsible = TRUE, solidHeader = TRUE,
        numericInput("g3_top_links", "Top n most connective genes:", value = 1000, width = "100%"),
        numericInput("g3_top_genes", "Show labels of top n genes:", value = 20, width = "100%"),
        numericInput("visNetwork_fontsize", "Fontsize of text:", value = 30, width = "100%"),
        numericInput("visNetwork_nodesize", "Nodesize:", value = 15, width = "100%"),
        numericInput("visNetwork_degree", "Highlight nearest nodes degree:", value = 1, width = "100%"),
        selectInput("visNetwork_smooth", "Smooth the links:", c("TRUE", "FALSE"), width = "100%"),
        actionButton("plot_genie3", "Plot GENIE3 NetWork", class = "plot-button", width = "100%")
      )
    )
  ),
  column(
    8,
    tabsetPanel(
      tabPanel(
        "Network Graph", style = "margin-top: 5px",
        column(
          9,
          fluidRow(
            column(6),
            column(
              6, align = "right",
              dropdownButton(
                numericInput('genie3_width', 'Figure Width:', min = 1, max = 20, value = 10, width = "100%"),
                numericInput('genie3_height', 'Figure Height:', min = 1, max = 20, value = 10, width = "100%"),
                downloadButton('g3_Pdf','Download .pdf', class = "btn btn-warning", width = "100%"),
                circle = FALSE, status = "danger", size = "sm", icon = icon("save"), width = "200px",
                right = TRUE, tooltip = tooltipOptions(title = "Click to download figures !")
              )
            )
          ),
          uiOutput("genie3_PlotUI")
        ),
        column(
          3,
          wellPanel(
            sliderInput("genie3_plot_width", "Figure Width (%):", min = 50, max = 100, value = 100, step = 2, width = "100%"),
            sliderInput("genie3_plot_height", "Figure Height (px):", min = 200, max = 1000, value = 496, step = 20, width = "100%")
          )
        )
      ),
      tabPanel(
        "Network Table", style = "margin-top: 5px; margin-left: 15px; margin-right: 15px",
        fluidRow(
          column(
            12,
            withSpinner(dataTableOutput("linkList")),
            conditionalPanel(
              "input.plot_genie3",
              downloadButton('linkList_download','Download .csv', width = "100%")
            )
          )
        )
      )
    )
  ),
  column(
    12,
    fluidRow(
      style = "background-color: rgb(248,249,250); border: 1px solid rgb(218,219,220); padding: 5px; margin:5px; border-radius: 15px;",
      column(
        4, style = "text-align:center;border-right: 2px solid white; padding-top:15px",
        tags$img(src = "images/demo/genie3_network_plot.png",
                 width = "100%")
      ),
      column(
        8, style = "text-align:justify;",
        h3("What is sample-to-sample distance (SSD) ?"),
        p("One of the pressing open problems of computational systems biology is the elucidation of the topology of 
        genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data.
        GENIE3 is a machine learning-based algorithm based on regression trees for inferring gene regulatory networks from expression data.
        GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems.
        In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of 
        all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. 
        The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link.
        Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed.
        It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable.")
      )
    )
  ),
  column(
    12,
    hr(),
    fluidRow(
      style = "margin-bottom:20px",
      column(3, align = "right", actionLink("pgenie3", "<< Previous", style = "font-size: 20px")),
      column(6, align = "center"),
      column(3, align = "left", actionLink("ngenie3", "Next >>", style = "font-size: 20px"))
    )
  )
)