Interactive heatmap using sequential data. Note the Google Chrome browser works fine, and Firefox may or may not. The Microsoft IE does not work at all.

server.R

library(shiny)
library(gplots) # Just for redgreen() function. Will be replaced sol.

lr2 <- as.matrix(read.delim("toy.txt", header = TRUE)) # 3 genes, 20 arrays, sequential data.
sigma <- apply(lr2, 1, sd); sigma <- sigma/max(sigma)
	
# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {
   
  datainput <- reactive(function() {  
    indgene <- which(sigma >= input$ratio)
    lr <- lr2[indgene, ]
    if (input$normm == "standardize") {
      t(scale(t(lr)))
    } else if (input$normm == "demean") {
      sweep(lr, 1, apply(lr, 1, mean))
    } else if (input$normm == "standardize") {
      lr
    } else if (input$normm == "demedian") {
      sweep(lr, 1, apply(lr, 1, median))
    } else if (input$normm == "mdstandardize") {
      lr <- sweep(lr, 1, apply(lr, 1, median))
      t(scale(t(lr), FALSE, TRUE))
    } else {
       lr
    }
  })  

  hclust1 <- function(x) hclust(x, method="average")
  hclust2 <- function(x) hclust(x, method="complete")
  hclust3 <- function(x) hclust(x, method="single")
  hcfun <- reactive(function() {
    switch(input$linkm,
      average  = hclust1,
      complete = hclust2,
      single   = hclust3)
  })
  
  colfun <- reactive(function() {
    switch(input$coltm, 
      greenred = greenred(75),
      bluered  = bluered(75))
  })

  output$myPlot <- reactivePlot(function() {
    heatmap.2(datainput(), col=colfun(), hclustfun = hcfun(), 
              # ColSideColors=rainbow(3)[as.factor(cl)], 
              # density.info="none", 
              key=TRUE, symkey=FALSE, trace="none", margins=c(10,10), scale = "none")
    title(main = list(paste(nrow(datainput()), "genes"), cex=1.5, col="blue", font=4)) 
    #plot(0,0, type="n"); text(0,0, as.character(input$normm))
  })

  #output$Boxplotg <- reactivePlot(function() {
  #  par(las=1, mar=c(5,10,0,2)+.1)
  #  boxplot(as.data.frame(t(datainput())), horizontal = TRUE, col = "bisque")
  #})

  output$Boxplots <- reactivePlot(function() {
    par(las=1, mar=c(5,10,0,2)+.1)
    boxplot(as.data.frame(datainput()), horizontal = TRUE, col = "bisque")
  })

  output$tableData <- reactiveTable(function() {
    data.frame(datainput()[1:min(10, nrow(datainput())), 1:min(5, ncol(datainput()))])
  })
})

toy.txt

g1	g2	g3	g4	g5	g6	g7	g8	g9	g10	g11	g12	g13	g14	g15	g16	g17	g18	g19	g20
-3to3	-3	-2.68421052631579	-2.36842105263158	-2.05263157894737	-1.73684210526316	-1.42105263157895	-1.10526315789474	-0.789473684210527	-0.473684210526316	-0.157894736842105	0.157894736842105	0.473684210526315	0.789473684210526	1.10526315789474	1.42105263157895	1.73684210526316	2.05263157894737	2.36842105263158	2.68421052631579	3
-2to2	-2	-1.78947368421053	-1.57894736842105	-1.36842105263158	-1.15789473684211	-0.947368421052632	-0.736842105263158	-0.526315789473684	-0.315789473684211	-0.105263157894737	0.105263157894737	0.315789473684211	0.526315789473684	0.736842105263158	0.947368421052631	1.15789473684211	1.36842105263158	1.57894736842105	1.78947368421053	2
-1to1	-1	-0.894736842105263	-0.789473684210526	-0.684210526315789	-0.578947368421053	-0.473684210526316	-0.368421052631579	-0.263157894736842	-0.157894736842105	-0.0526315789473685	0.0526315789473684	0.157894736842105	0.263157894736842	0.368421052631579	0.473684210526316	0.578947368421053	0.684210526315789	0.789473684210526	0.894736842105263	1

ui.R

library(shiny)

# Define UI for application that plots random distributions 
shinyUI(pageWithSidebar(
  
  # Application title
  headerPanel("Shiny heatmap of a sequential data"),
  
  # Sidebar with a slider input for number of observations
  sidebarPanel(
    sliderInput("ratio", 
                "Filter by Variance (sigma/sigma_max):", 
                min = 0, 
                max = 1, 
                value = .1, step = 0.01),
    br(),  			
    radioButtons("normm", "Normalization method:",
                 c("None" = "none",
                   "Mean=0" = "demean",
                   "Mean=0,Var=1" = "standardize",
                   "Median=0" = "demedian",
                   "Median=0,Var=1" = "mdstandardize"),
                 selected = "None"),
    br(),				
    radioButtons("linkm", "Linkage method:",
                 c("Average linkage" = "average",
                   "Complete/Maximum linkage" = "complete",
                   "Single/Minimum linkage" = "single")),
    br(),				
    radioButtons("distm", "Distance method (Only implemented Euclidean):",
                 c("Euclidean" = "elcu",
                   "1-Correlation" = "corr")),
    br(),				
    radioButtons("coltm", "Color preference:",
                 c("Green to Red" = "greenred",
                   "Blue to Red" = "bluered"),
                 selected = "Blue to Red")
    #tags$hr(),
    #fileInput('file1', 'Choose tab-delimited data file containing header',
    #          accept=c('text/csv', 'text/comma-separated-values,text/plain'))
  ),
  
  # Show a plot of the generated distribution
  mainPanel(
    tabsetPanel(
      tabPanel("Heatmap", plotOutput("myPlot", height="800px")), 
      # tabPanel("Boxplot for genes", plotOutput("Boxplotg", height="800px")),
      tabPanel("Boxplot for samples", plotOutput("Boxplots", height="800px")),
      tabPanel("Data preview", tableOutput("tableData"))
    )

    # plotOutput("myPlot", height="800px")
  )
))