## ----eval = FALSE-------------------------------------------------------------
#  library(airway)
#  data(airway)
#  se = airway
#  library(DESeq2)
#  dds = DESeqDataSet(se, design = ~ dex)
#  keep = rowSums(counts(dds)) >= 10
#  dds = dds[keep, ]
#  
#  dds$dex = relevel(dds$dex, ref = "untrt")
#  
#  dds = DESeq(dds)
#  res = results(dds)
#  res = as.data.frame(res)

## ----eval = FALSE-------------------------------------------------------------
#  library(InteractiveComplexHeatmap)
#  library(ComplexHeatmap)
#  library(circlize)
#  library(GetoptLong)
#  
#  env = new.env()
#  
#  make_heatmap = function(fdr = 0.01, base_mean = 0, log2fc = 0) {
#  	l = res$padj <= fdr & res$baseMean >= base_mean &
#                          abs(res$log2FoldChange) >= log2fc; l[is.na(l)] = FALSE
#  
#  	if(sum(l) == 0) return(NULL)
#  
#  	m = counts(dds, normalized = TRUE)
#  	m = m[l, ]
#  
#  	env$row_index = which(l)
#  
#  	ht = Heatmap(t(scale(t(m))), name = "z-score",
#  	    top_annotation = HeatmapAnnotation(
#  	        dex = colData(dds)$dex,
#  	        sizeFactor = anno_points(colData(dds)$sizeFactor)
#  	    ),
#  	    show_row_names = FALSE, show_column_names = FALSE, row_km = 2,
#  	    column_title = paste0(sum(l), " significant genes with FDR < ", fdr),
#  	    show_row_dend = FALSE) +
#  	    Heatmap(log10(res$baseMean[l]+1), show_row_names = FALSE, width = unit(5, "mm"),
#  	        name = "log10(baseMean+1)", show_column_names = FALSE) +
#  	    Heatmap(res$log2FoldChange[l], show_row_names = FALSE, width = unit(5, "mm"),
#  	        name = "log2FoldChange", show_column_names = FALSE,
#  	        col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")))
#  	ht = draw(ht, merge_legend = TRUE)
#      ht
#  }
#  
#  # make the MA-plot with some genes highlighted
#  make_maplot = function(res, highlight = NULL) {
#      col = rep("#00000020", nrow(res))
#      cex = rep(0.5, nrow(res))
#      names(col) = rownames(res)
#      names(cex) = rownames(res)
#      if(!is.null(highlight)) {
#          col[highlight] = "red"
#          cex[highlight] = 1
#      }
#      x = res$baseMean
#      y = res$log2FoldChange
#      y[y > 2] = 2
#      y[y < -2] = -2
#      col[col == "red" & y < 0] = "darkgreen"
#      par(mar = c(4, 4, 1, 1))
#  
#      suppressWarnings(
#          plot(x, y, col = col,
#              pch = ifelse(res$log2FoldChange > 2 | res$log2FoldChange < -2, 1, 16),
#              cex = cex, log = "x",
#              xlab = "baseMean", ylab = "log2 fold change")
#      )
#  }
#  
#  # make the volcano plot with some genes highlited
#  make_volcano = function(res, highlight = NULL) {
#      col = rep("#00000020", nrow(res))
#      cex = rep(0.5, nrow(res))
#      names(col) = rownames(res)
#      names(cex) = rownames(res)
#      if(!is.null(highlight)) {
#          col[highlight] = "red"
#          cex[highlight] = 1
#      }
#      x = res$log2FoldChange
#      y = -log10(res$padj)
#      col[col == "red" & x < 0] = "darkgreen"
#      par(mar = c(4, 4, 1, 1))
#  
#      suppressWarnings(
#          plot(x, y, col = col,
#              pch = 16,
#              cex = cex,
#              xlab = "log2 fold change", ylab = "-log10(FDR)")
#      )
#  }

## ----eval = FALSE-------------------------------------------------------------
#  library(shiny)
#  library(shinydashboard)
#  body = dashboardBody(
#      fluidRow(
#          column(width = 4,
#              box(title = "Differential heatmap", width = NULL, solidHeader = TRUE, status = "primary",
#                  originalHeatmapOutput("ht", height = 800, containment = TRUE)
#              )
#          ),
#          column(width = 4,
#          	id = "column2",
#              box(title = "Sub-heatmap", width = NULL, solidHeader = TRUE, status = "primary",
#                  subHeatmapOutput("ht", title = NULL, containment = TRUE)
#              ),
#              box(title = "Output", width = NULL, solidHeader = TRUE, status = "primary",
#                  HeatmapInfoOutput("ht", title = NULL)
#              ),
#              box(title = "Note", width = NULL, solidHeader = TRUE, status = "primary",
#                  htmlOutput("note")
#              ),
#          ),
#          column(width = 4,
#              box(title = "MA-plot", width = NULL, solidHeader = TRUE, status = "primary",
#                  plotOutput("ma_plot")
#              ),
#              box(title = "Volcanno plot", width = NULL, solidHeader = TRUE, status = "primary",
#                  plotOutput("volcanno_plot")
#              ),
#              box(title = "Result table of the selected genes", width = NULL, solidHeader = TRUE, status = "primary",
#                  DTOutput("res_table")
#              )
#          ),
#          tags$style("
#              .content-wrapper, .right-side {
#                  overflow-x: auto;
#              }
#              .content {
#                  min-width:1500px;
#              }
#          ")
#      )
#  )

## ----eval = FALSE-------------------------------------------------------------
#  library(DT)
#  library(GetoptLong) # for qq() function
#  brush_action = function(df, input, output, session) {
#  
#      row_index = unique(unlist(df$row_index))
#      selected = env$row_index[row_index]
#  
#      output[["ma_plot"]] = renderPlot({
#          make_maplot(res, selected)
#      })
#  
#      output[["volcanno_plot"]] = renderPlot({
#          make_volcano(res, selected)
#      })
#  
#      output[["res_table"]] = renderDT(
#          formatRound(datatable(res[selected, c("baseMean", "log2FoldChange", "padj")], rownames = TRUE), columns = 1:3, digits = 3)
#      )
#  
#      output[["note"]] = renderUI({
#          if(!is.null(df)) {
#              HTML(qq("<p>Row indices captured in <b>Output</b> only correspond to the matrix of the differential genes. To get the row indices in the original matrix, you need to perform:</p>
#  <pre>
#  l = res$padj <= @{input$fdr} &
#      res$baseMean >= @{input$base_mean} &
#      abs(res$log2FoldChange) >= @{input$log2fc}
#  l[is.na(l)] = FALSE
#  which(l)[row_index]
#  </pre>
#  <p>where <code>res</code> is the complete data frame from DESeq2 analysis and <code>row_index</code> is the <code>row_index</code> column captured from the code in <b>Output</b>.</p>"))
#          }
#      })
#  }

## ----eval = FALSE-------------------------------------------------------------
#  ui = dashboardPage(
#      dashboardHeader(title = "DESeq2 results"),
#      dashboardSidebar(
#      	selectInput("fdr", label = "Cutoff for FDRs:", c("0.001" = 0.001, "0.01" = 0.01, "0.05" = 0.05)),
#      	numericInput("base_mean", label = "Minimal base mean:", value = 0),
#      	numericInput("log2fc", label = "Minimal abs(log2 fold change):", value = 0),
#      	actionButton("filter", label = "Generate heatmap")
#      ),
#      body
#  )

## ----eval = FALSE-------------------------------------------------------------
#  server = function(input, output, session) {
#  	observeEvent(input$filter, {
#  		ht = make_heatmap(fdr = as.numeric(input$fdr), base_mean = input$base_mean, log2fc = input$log2fc)
#  		if(!is.null(ht)) {
#  		    makeInteractiveComplexHeatmap(input, output, session, ht, "ht",
#  		        brush_action = brush_action)
#  		} else {
#              # The ID for the heatmap plot is encoded as @{heatmap_id}_heatmap, thus, it is ht_heatmap here.
#  			output$ht_heatmap = renderPlot({
#  				grid.newpage()
#  				grid.text("No row exists after filtering.")
#  			})
#  		}
#  	}, ignoreNULL = FALSE)
#  }

## ----eval = FALSE-------------------------------------------------------------
#  shinyApp(ui, server)

## ----eval = FALSE-------------------------------------------------------------
#  library(DESeq2)
#  library(airway)
#  data(airway)
#  
#  dds = DESeqDataSet(airway, design = ~ dex)
#  dds = DESeq(dds)
#  
#  interactivate(dds)

