add reactive when under 2000 and event when upper to this treshold

function/heatmtruncated.R

@@ -1,8 +1,25 @@
 require(dplyr)
 require(RColorBrewer)
-#gsub("[^A-Z|_]", "", x) 
 
-#' distcor is a function that computes the distance correlation 
+
+## Add jackknife correlation good for false positives with the pval methods add citation ....
+
+
+#' distcos is a function that computes the cosine similarity for a given matrix
+#'
+#' @param x 
+#'
+#' @return a matrix distance
+#' 
+#' @export
+
+distcos <- function(x){ #Jonathan Chang
+  as.dist(1 - x%*%t(x)/(sqrt(rowSums(x^2) %*% t(rowSums(x^2))))) 
+}
+
+
+
+#' distcor is a function that computes the pearson correlation 
 #'
 #' @param x 
 #'
@@ -13,7 +30,7 @@ require(RColorBrewer)
 distcor<-function(x) {as.dist(1-cor(t(x),use="pairwise.complete.obs"))}
 
 
-#' disteuc is a function that computes the euclidean correlation
+#' disteuc is a function that computes the euclidean distance matrices
 #'
 #' @param x 
 #'
@@ -290,8 +307,7 @@ plotHeatmaps=function(exprData,geneSet,groups,workingPath=getwd(),fileType="png"
 
   #RowSideColor: color palette to be used for rowSide cluster colors
   # can also be gray.colors(k, start = 0.2, end = 0.9) to get k colors of gray scale
-
-
+
   if(is.null(showcol))
     showcol = F
 

server/checkboxcontrast.R

@@ -39,9 +39,11 @@ observeEvent(input$noTests, {
 #'
 
 
-choix_test <- eventReactive(input$heatm, {
+#choix_test <- eventReactive(input$heatm, {
+choix_test <- reactive({
   return(input$test)
-}, ignoreNULL = F)
+})
+#, ignoreNULL = F)
 
 
 # choix_test <- reactive({

server/checkboxgrp.R

@@ -45,9 +45,12 @@ observeEvent(input$noIndividus, {
 #'
 
 choix_grp <- reactive({
+  req(input$indiv)
+
   inFile <- input$file
   if (is.null(inFile))
     return(NULL)
+
   return(input$indiv)
 })
 
@@ -74,12 +77,14 @@ list_ind <- reactive({
 #' @return \newgroup an eventreactive factor with the corresponding groups selected
 #'
 
-new_group <- eventReactive(input$heatm, {
+#new_group <- eventReactive(input$heatm, {
+new_group <- reactive({
   inFile <- input$file
   if (is.null(inFile))
     return(NULL)
   csvf()[[2]][csvf()[[2]]$Grp %in% choix_grp(),]
-}, ignoreNULL = F)
+})
+#}, ignoreNULL = F)
 
 
 

server/groupcolor.R

@@ -15,6 +15,7 @@ mycolgrp <- reactive  ({
     unique() %>%
     droplevels()
 
+
   return(mygrpcol)
 })
 

server/heatmapshiny.R

@@ -3,66 +3,8 @@
 ###############################
 
 
-hm <- eventReactive(input$heatm, {
-  heatmapfinal(isplot = F)
-})
-
-#' heatmapfinal is an isolate function that only react to a user's click on the heatmap button 
-#' 
-#' @param heatmapobj[[1]] a data frame with all the individuals selected
-#' @param formated  a data frame with the indexes corresponding to the sigificant genes
-#' @param new_group  a data frame with the corresponding groups 
-#' @param workingPath the current user's repository 
-#' @param my_palette a vector of colors 
-#' @param k a numeric value which aim is to defined the treshold value to cut the dendogram input$clusters
-#' @param Rowdistfun a character value set by the user to defined the method to calculate the dendogram matrix distance for the genes input$dist
-#' @param Coldistfun a character value set by the user to defined the method to calculate the dendogram matrix distance for the contrasts input$dist
-#' @param mycex a numeric value which aim is to change the size of the legend in the heatmap defined by the user input$legsize
-#' @param cexrow  a numeric value to change the size of the police legend for the rows input$rowsize
-#' @param cexcol a numeric value to change the size of the police legend for the columns input$colsize
-#' @param meanGrp a boolean value to compute or not the mean of each contrasts in the heatmap input$meangrp
-#' @param mypal a list of values 
-#' @param showcol a boolean value used to hide or show the colnames input$colname
-#' @param showrow a boolean value used to hide or show the rownames input$rowname
-#' @param genename a data frame 
-#' @param notplot a boolean value for applying dev.off or not on the heatmap
-#' @param rowv  dendogram object
-#' @param ColOrd  positive numbers, used as cex.axis in for the row or column axis labeling
-#' @param gpcol  matrix with colors associated to each groups 
-#' @param gpcolr  matrix with gray color depending on the clusters
-#' @param distfunTRIX function that computes whether euclidian or pearson for Hierarchical Clustering
-#'
-#' @return  a data frame with the cluster and the corresponding genes 
-#' 
-#' @export
-#' 
 
 
-heatmapfinal <- function(isplot  = T) {
-  plotHeatmaps(
-    heatmapobj[[1]],
-    formated(),
-    droplevels(new_group()$Grp),
-    workingPath = wd_path,
-    my_palette = colorRampPalette(c(
-      choix_col1(), my_intermediate(), choix_col3()
-    ))(n = 75),
-    mycex = input$legsize ,
-    cexrow = input$rowsize ,
-    cexcol = input$colsize ,
-    mypal =  unlist(colors()),
-    showcol = colname(),
-    showrow = rowname(),
-    genename =  csvf()[[3]],
-    notplot = isplot,
-    rowv = heatmapobj[[4]],
-    ColvOrd = heatmapobj[[3]],
-    gpcol = heatmapobj[[5]],
-    gpcolr = heatmapobj[[6]],
-    distfunTRIX = heatmapobj[[2]]
-  )
-}
-
 #' rowname is a reactive function which aim is to hide or show the rownames
 #'
 #' @param input$rowname  a boolean radio button input
@@ -95,132 +37,34 @@ colname <- reactive({
 
 
 heatmapobj <- NULL # declare outside the observeEvent
+formatidus <- NULL
 
-observeEvent(input$heatm, {
-
-
-  #' hmbis is an event reactive function that pre-computed hierarchical clustering on microarray data 
-  #'
-  #' @param new_data a data frame with all the individuals selected
-  #' @param formated  a data frame with the indexes corresponding to the sigificant genes
-  #' @param new_group  a data frame with the corresponding groups 
-  #' @param workingPath the current user's repository 
-  #' @param k a numeric value which aim is to defined the treshold value to cut the dendogram input$clusters
-  #' @param Rowdistfun a function used to compute the distance for the rows
-  #' @param Coldistfun a function used to compute the distance for the columns
-  #' @param meanGrp a boolean value to computes the mean for each groups; default = F
-  #'
-  #' @return  a list of objects which aim is to being passed as argument in the plotHeatmaps function
-  #' 
-  #' @export
-  #' 
-
-
-  hmbis <- eventReactive(input$heatm, {
-
-    isolate( # isolate in order to avoid that reactive values update the heatmap 
-      truncatedhat(
-        data.matrix(new_data()),
-        formated(),
-        droplevels(new_group()$Grp),
-        workingPath = wd_path,
-        k = input$clusters,
-        Rowdistfun = input$dist ,
-        Coldistfun = input$dist,
-        meanGrp = input$meangrp
-      )
-    )
-  })
-
-  heatmapobj <<- hmbis() # a static variable 
+observe({
 
-
-
-  output$distPlot <- renderPlot({
-    isolate({
-      if (!is.null(formated()))
-        withProgress(message = 'Plotting heatmap:', # Add sliderbar when loading heatmap
-                     value = 0,
-                     {
-                       n <- NROW(formated()) #number of row in the formated dataframe
-
-                       for (i in 1:n) {
-                         incProgress(1 / n, detail = "Please wait...")
-                       }
-
-                       heatmapfinal(isplot = F)
-                     })
-    })
-  }, width = 900 , height = 1200, res = 100)
-
-
-  output$save <- downloadHandler(filename <- function() {
-    paste0(basename(file_path_sans_ext("myfile")),
-           '_heatmap.',
-           input$form,
-           sep = '')
-  },
-  content <- function(file) {
-    if (input$form == "emf")
-
-      emf(
-        file,
-        width = 7,
-        height = 7,
-        pointsize = 12,
-        coordDPI = 300
-      )
-
-    else if (input$form == "png")
-      png(
-        file,
-        width = 900,
-        height = 1200,
-        units = "px",
-        pointsize = 12,
-        res = 100
-      )
+  heatid <- input$matrixapp
+  if (grepl("Heatmap", heatid)) {
+    formatidus <<- length(formated())
+    if (formatidus < 2000)
+      source(file.path("server", "plotreact.R"), local = TRUE)$value #
     else
-      eps(file,
-          width = 7,
-          height = 7)
-
-    if (!is.null(formated()))
-      withProgress(message = 'Saving heatmap:',
-                   value = 0,
-                   {
-                     n <- NROW(formated())
-
-                     for (i in 1:n) {
-                       incProgress(1 / n, detail = "Please wait...")
-                     }
-
-                     heatmapfinal(isplot = F)
-
-                   })
-
-    dev.off()
-
-
-  })
-
+      source(file.path("server", "plotreact2.R"), local = TRUE)$value #
+  }
 
   output$downloadcut <- downloadHandler(
     filename = function() {
-      paste(basename(file_path_sans_ext(input$filename)), '_clustered', '.csv', sep ='')
+      paste(basename(file_path_sans_ext(input$filename)),
+            '_clustered',
+            '.csv',
+            sep = '')
     },
     content = function(file) {
       write.csv(heatmapfinal(isplot = T), file, row.names = FALSE)
     }
   )
 
-  output$clustering <-
-    renderDataTable(heatmapfinal(isplot = T)) # Summary of the significant genes depending on the pvalue with FC set to (1.2,2,4,6,10)
-
+  output$clustering <- renderDataTable(heatmapfinal(isplot = T)) # Summary of the significant genes depending on the pvalue with FC set to (1.2,2,4,6,10)
 })
 
-
-
 # cutedhm <- reactive({
 #   req(hm())
 #   cut02 = cut(hm()$rowDendrogram, h = input$cutheatm)
@@ -234,4 +78,4 @@ observeEvent(input$heatm, {
 #   dfclust = heatmtoclust( hm(), formated(), data.matrix(new_data()), csvf()[[3]], input$cutheatm)
 #
 #   return(dfclust)
-# })
+# })

server/indexselected.R

@@ -5,26 +5,29 @@
 #' @param input$method2 character method, default = BH
 #' @param input$pval numeric pvalue
 #' @param input$maxgen numeric maxgen, default = NULL
-#' 
+#'
 #' @return \formated a reactive data frame with the indexes corresponding to the sigificant genes
 #'
 
 
 formated <- reactive({
 
-  #req(!is.null(user_group()))
-  #treated = formating(new_test(), csvf()[[1]], input$pval)
-  treated = decTestTRiX(
-    user_group()[[1]],
-    user_group()[[2]],
-    user_group()[[3]],
-    DEGcutoff = input$pval,
-    FC = input$fc,
-    cutoff_meth = input$method2
-
-    ,maxDE = input$maxgen
-
-  )
+  req(user_group())
+
+  df <- csvf()
+  if (is.null(df))
+    return(NULL)
+
+  else
+    treated = decTestTRiX(
+      user_group()[[1]],
+      user_group()[[2]],
+      user_group()[[3]],
+      DEGcutoff = input$pval,
+      FC = input$fc,
+      cutoff_meth = input$method2,
+      maxDE = input$maxgen
+
+    )
   return(treated)
 })
-