inst/shiny/modules/11-ui-wgcna-detect-module.R

In goushixue/QRseq: Quickly RNA-seq Analysis Platform

fluidRow(
  style = "margin-left: 10px; margin-right:10px;",
  box(
    title = "blockwiseModules:", width = 4, collapsible = TRUE, solidHeader = TRUE,
    switchInput("wgcna_cache", "Use Cache", value = F,
                onStatus = "success", offStatus = "danger", inline = T,labelWidth = "100px"),
    numericInput("soft_power", "soft-thresholding power value:", value = 10, width = "100%"),
    # numericInput("maxBlockSize", "Maximum block size for module detection:", value = 5000, width = "100%"),
    numericInput("minModuleSize", "Minimum module size for module detection:", value = 30, width = "100%"),
    selectInput("blockwise_networkType", "networkType:", choices = c("unsigned", "signed", "signed hybrid"), width = "100%"),
    actionButton("blockwise_modal_but", "Additional Parameters...", width = "100%",
                 style = "background-color: rgb(255,255,255);text-align:left;margin-bottom:10px", icon = icon("plus-square")),
    br(),
    actionButton("moldue_detect", "Start moldue detect", width = "100%", class = "run-button")
  ),
  column(
    8,
    tabsetPanel(
      tabPanel(
        "Cluster Dendrogram",
        column(
          9,
          fluidRow(
            column(
              12, style = "padding-top:5px;",
              column(6),
              column(
                6, align = "right", 
                dropdownButton(
                  numericInput('plotDendro_width', 'Figure Width:', value = 10, width = "100%"),
                  numericInput('plotDendro_height', 'Figure Height:', value = 5, width = "100%"),
                  downloadButton('plotDendro_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("wgcna_dendroUI")
        ),
        column(
          3, style = "padding-top:5px;",
          wellPanel(
            sliderInput("wgcna_dendro_width", "Figure Width (%):", min = 50, max = 100, value = 100, step = 2, width = "100%"),
            sliderInput("wgcna_dendro_height", "Figure Height (px):", min = 200, max = 1000, value = 358, step = 2, width = "100%")
          )
        )
      ),
      tabPanel(
        "ModuleGene to color table",
        conditionalPanel(
          "input.moldue_detect", style = "padding:10px;",
          withSpinner(dataTableOutput("moduleGene_table")),
          downloadButton('moduleGene_table_csv','Download .csv', class = "btn", width = "100%")
        )
      )
    )
  ),
  bsModal(
    "blockwise_modal", "Additional Parameters", "blockwise_modal_but", size = "large",
    column(
      12,
      style = "text-align:justify;color:black;background-color:lavender;border-radius:10px;border:1px solid black;", br(),
      h5("Additional Parameters of 'blockwiseModules':"), hr(),
      fluidRow(
        column(
          6,
          numericInput("blockSizePenaltyPower", "blockSizePenaltyPower:", value = 5, width = "100%"),
          numericInput("maxPOutliers", "maxPOutliers:", value = 1, width = "100%"),
          numericInput("quickCor", "quickCor:", value = 0, width = "100%"),
          numericInput("detectCutHeight", "detectCutHeight:", value = 0.995, width = "100%"),
          selectInput("impute", "impute:", choices = c("TRUE", "FALSE"), width = "100%"),
          selectInput("corType", "corType:", choices = c("pearson", "bicor"), width = "100%"),
          selectInput("TOMDenom", "TOMDenom:", choices = c("min", "mean"), width = "100%")
        ),
        column(
          6,
          numericInput("reassignThreshold", "reassignThreshold:", value = 1e-6, width = "100%"),
          numericInput("minCoreKME", "minCoreKME:", value = 0.5, width = "100%"),
          numericInput("minKMEtoStay", "minKMEtoStay:", value = 0.3, width = "100%"),
          numericInput("mergeCutHeight", "mergeCutHeight:", value = 0.15, width = "100%"),
          selectInput("TOMType", "TOMType:", width = "100%", selected = "signed",
                      choices = c("none", "unsigned", "signed", "signed Nowick", "unsigned 2", "signed 2", "signed Nowick 2")),
          selectInput("deepSplit", "deepSplit:", choices = c(0, 1, 2, 3, 4), selected = 2, width = "100%"),
          selectInput("pearsonFallback", "pearsonFallback:", choices = c("individual", "all", "none"), width = "100%")
        )
      )
    )
  ),
  column(
    12, style = "padding:0px;",
    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/wgcna_dendrogram.png",
                 width = "100%")
      ),
      column(
        8, style = "text-align:justify;",
        h3("How does WGCNA identify gene modules?"),
        p("WGCNA assume that modules are groups of genes whose expression profiles are highly correlated across the samples. 
          To group genes with coherent expression profiles into modules, it use average linkage hierarchical clustering coupled 
          with the TOM-based dissimilarity. It also used the TOM-based dissimilarity in conjunction with partitioning around
          medoid clustering. In WGCNA, gene modules correspond to branches of the hierarchical clustering tree (dendrogram).
          The simplest (not necessarily best) method is to choose a height cutoff to cut branches off the tree. 
          The resulting branches correspond to gene modules, i.e. sets of highly co-expressed genes. 
          The parameter mergeCutHeight (default = 0.25) is the threshold for merging of modules.
          The parameter soft-thresholding power should choose the most appropriate value based on the results detected in the previous step. 
          If the result of the previous step is not satisfactory, you can try to select the value according to experience.
          For the parameter networkType, we set the default value to 'signed', as suggested by the author. See the artical", 
          a(em("Signed or unsigned: which network type is preferable?"), target = "_blank", 
            href = "https://peterlangfelder.com/2018/11/25/signed-or-unsigned-which-network-type-is-preferable/"))
      )
    )
  ),
  column(
    12,
    hr(),
    fluidRow(
      style = "margin-bottom:20px",
      column(3, align = "right", actionLink("pWGCNA_3", "<< Previous", style = "font-size: 20px")),
      column(6, align = "center"),
      column(3, align = "left", actionLink("nWGCNA_3", "Next >>", style = "font-size: 20px"))
    )
  )
)