Merge pull request #2 from pworinger/master

Add plots to analyse the differential abundances and new visualisations

LoadPackages.R

-options(download.file.method = 'wget', bitmapType='cairo')
+#options(download.file.method = 'wget', bitmapType='cairo')
+options(bitmapType='cairo')
 
 if (!require("Rcpp")){
   install.packages("Rcpp",  repos="http://cran.univ-paris1.fr/")
@@ -65,11 +66,11 @@ if(!require(treeWeightD3)){
    devtools::install_github('pierreLec/treeWeightD3')
    library(treeWeightD3)
 }
-if (!require(BiocInstaller)){
-  source("https://bioconductor.org/biocLite.R")
-  biocLite("BiocInstaller")
-  library(BiocInstaller)
-}
+# if (!require(BiocInstaller)){
+#   source("https://bioconductor.org/biocLite.R")
+#   biocLite("BiocInstaller")
+#   library(BiocInstaller)
+# }
 
 if (!require(d3heatmap)) {
   devtools::install_github('aghozlane/d3heatmap')
@@ -83,7 +84,8 @@ library(biomformat)
 }
 
 if (!require(scatterD3)) {
-  devtools::install_github('aghozlane/scatterD3')
+  #devtools::install_github('aghozlane/scatterD3')
+  install.packages("scatterD3")
   library(scatterD3)
 }
 
@@ -109,7 +111,7 @@ if (!require(shinyjs)) {
 }
 
 if(!require(d3vennR)){
-  install_github("timelyportfolio/d3vennR")
+  devtools::install_github("timelyportfolio/d3vennR")
   library(d3vennR)
 }
 
@@ -129,8 +131,7 @@ if (!require(gplots)) {
 }
 
 if (!require(DESeq2)) {
-  source("https://bioconductor.org/biocLite.R")
-  biocLite("DESeq2")
+  devtools::install_github("aghozlane/DESeq2shaman")
   library(DESeq2)
 }
 
@@ -223,4 +224,47 @@ if (!require("htmltools")){
 #   library(plotly)  
 # }
 
+if (!require("rAmCharts")){
+  install.packages("rAmCharts")
+  library(rAmCharts)
+}
+
+if(!require("colourpicker")){
+  install.packages("colourpicker")
+  library(colourpicker)
+}
+
+if(!require("data.table")){
+  install.packages("data.table")
+  library(data.table)
+}
+
+if(!require("UpSetR")){
+  install.packages("UpSetR")
+  library(UpSetR)
+}
+
+if(!require("ggrepel")){
+  install.packages("ggrepel")
+  library(ggrepel)
+}
+
+# if(!require("networkD3")){
+#   install.packages("networkD3")
+#   library(networkD3)
+# }
+
+if(!require("igraph")){
+  install.packages("igraph")
+  library(igraph)
+}
+
+if(!require("visNetwork")){
+  install.packages("visNetwork")
+  library(visNetwork)
+}
 
+if(!require("SummarizedExperiment")){
+  BiocManager::install("SummarizedExperiment")
+  library(SummarizedExperiment)
+}
\ No newline at end of file

Rfunctions/CompPlot.R

@@ -5,20 +5,22 @@
 ##############################
 ##       HEATMAP
 ##############################
-Plot_Visu_Heatmap_FC <- function(input,BaseContrast,resDiff,export=FALSE){
+Plot_Visu_Heatmap_FC <- function(input, BaseContrast, resDiff, ContrastListDebounce, SelectTaxoPlotCompDebounce, export=FALSE){
   
   res = NULL
-  SelContrast = input$ContrastList_table_FC
+  #SelContrast = input$ContrastList_table_FC
+  SelContrast = ContrastListDebounce()
   selcontrast_matrix = as.matrix(BaseContrast[,SelContrast])
   colnames(selcontrast_matrix) = SelContrast
   log2FC = Get_log2FC_padj(input,selcontrast_matrix,resDiff, info = NULL)$log2FC
   
-  
   if(!is.null(log2FC) && length(SelContrast)>=2)
   { 
     cont = which(colnames(log2FC)%in%SelContrast)
-    log2FC = log2FC[,SelContrast] 
-    ind_taxo = input$selectTaxoPlotComp
+    log2FC = log2FC[,SelContrast]
+    ###
+    ind_taxo = SelectTaxoPlotCompDebounce()
+    #ind_taxo = input$selectTaxoPlotComp
     ind = rownames(log2FC)%in%ind_taxo
     log2FC = as.matrix(log2FC[ind,])
     
@@ -41,21 +43,260 @@ Plot_Visu_Heatmap_FC <- function(input,BaseContrast,resDiff,export=FALSE){
   return(res)
 }
 
+##############################
+##      P VALUE DENSITY PLOT
+##############################
+Plot_pValue_Density <- function(input, BaseContrast, resDiff, ContrastListDebounce, alphaVal){
+  res = NULL
+  #SelContrast = input$ContrastList_table_FC
+  SelContrast = ContrastListDebounce()
+  if(length(SelContrast)>=1){
+  selcontrast_matrix = as.matrix(BaseContrast[,SelContrast])
+  colnames(selcontrast_matrix) = SelContrast
+  
+  padj = data.frame(na.omit(Get_log2FC_padj(input,selcontrast_matrix,resDiff)$padj))
+  
+  data <- data.frame()
+  for (cont in SelContrast){
+    data_cont <- data.frame(padj[,cont])
+    data_cont$contrast <- cont
+    colnames(data_cont) <- c("padj","contrast")
+    data <- rbind(data, data_cont)
+  }
+  data$contrast <- factor(data$contrast, levels = SelContrast)
+                                                                                                                          # uncomment to zoom (X axis between 0 and input$alphaVal)                               (use "+ xlim(0,as.numeric(alphaVal))" instead, to redraw geom_density with only data under threshold)
+  p <- ggplot(data, aes(x = padj, color = contrast, fill = contrast)) + geom_density(alpha = input$fillOpacity, size = input$lineWidth) + theme_minimal() #+ coord_cartesian(xlim = c(0,as.numeric(alphaVal)))
+  p <- p + theme(axis.title = element_text(size = input$FontSizepValueDensity), 
+                 axis.text = element_text(size = input$FontSizepValueDensity),
+                 legend.title = element_text(size = input$FontSizepValueDensity),
+                 legend.text = element_text(size = input$FontSizepValueDensity))
+  res = p
+  }
+  return(res)
+}
+
+##############################
+##      LOGIT PLOT
+##############################
+Plot_Comp_Logit <- function(input, BaseContrast, resDiff, SelectTaxoPlotCompDebounce, export = FALSE)
+  {
+  plot = NULL
+  if(input$Contrast1 != input$Contrast2 && input$Contrast1 != "..." && input$Contrast2 != "..."){
+    SelContrast = c(input$Contrast1, input$Contrast2)
+    selcontrast_matrix = as.matrix(BaseContrast[,SelContrast])
+    colnames(selcontrast_matrix) = SelContrast
+    padj = na.omit(Get_log2FC_padj(input,selcontrast_matrix,resDiff)$padj)
+   
+    #data to plot
+    x <- padj[,input$Contrast1]
+    logit_x <- log(x/(1-x))
+    y <- padj[,input$Contrast2]
+    logit_y <- log(y/(1-y))
+    
+    #classification for colours
+    alpha_logit <- log(as.numeric(input$AlphaVal)/(1-as.numeric(input$AlphaVal)))
+    x_signif <- logit_x < alpha_logit
+    y_signif <- logit_y < alpha_logit
+    matrix <- cbind(x_signif, y_signif)
+    color_var <-
+      apply(matrix, 1, function(point)
+        if (point[[1]]) {
+          if (point[[2]]) {"Significant for both contrasts"}
+          else {"Significant for contrast 1"}
+        } 
+        else{
+          if (point[[2]]) {"Significant for contrast 2"}
+          else{"Not significant"}
+          }
+      )
+    
+    #labels
+    names <- row.names(padj)
+    ###
+    points_to_label <- SelectTaxoPlotCompDebounce()
+    #points_to_label <- input$selectTaxoPlotComp
+    labels <-
+      sapply(names, function(name)
+        if (is.element(name, points_to_label)) {
+          points_to_label[match(name, points_to_label)]
+        } else{
+          ""
+        })
+    
+    if(!export){
+      plot <- scatterD3(x = logit_x, 
+                        y = logit_y,
+                        xlab = paste("logit p value", input$Contrast1),
+                        ylab = paste("logit p value", input$Contrast2),
+                        hover_opacity = 1,
+                        tooltip_text = row.names(padj),
+                        # xlim = xlimits,
+                        # ylim = ylimits,
+                        lines = if (input$showSignifThresholdsLogitPlot) {
+                          if(input$showDiagonal){
+                            data.frame(
+                              slope = c(Inf, 0, 1),
+                              intercept = c(alpha_logit,alpha_logit,0),
+                              stroke = "black",
+                              stroke_width = input$linesWidthLogitPlot,
+                              stroke_dasharray = 5
+                            )
+                          }
+                          else{
+                          data.frame(
+                            slope = c(Inf, 0),
+                            intercept = c(alpha_logit,alpha_logit),
+                            stroke = "black",
+                            stroke_width = input$linesWidthLogitPlot,
+                            stroke_dasharray = 5
+                          )}
+                        }
+                        else{if (input$showDiagonal){
+                          data.frame(
+                            slope = c(1),
+                            intercept = c(0),
+                            stroke = "black",
+                            stroke_width = input$linesWidthLogitPlot,
+                            stroke_dasharray = 5
+                          )}
+                        else{NULL}},
+                        fixed = input$fixed11,
+                        col_var = color_var,
+                        col_lab = if(input$showLegendLogit){"Legend"}else{NA}
+                        ,
+                        colors = c(
+                          "Not significant" = input$colour01,
+                          "Significant for contrast 1" = input$colour02,
+                          "Significant for contrast 2" = input$colour03,
+                          "Significant for both contrasts" = input$colour04
+                        ),
+                        lab = labels,
+                        point_opacity = input$pointOpacityLogit,
+                        point_size = input$pointSizeLogit,
+                        axes_font_size = paste(input$axisFontSizeLogit, "0%", sep = ""),
+                        labels_size = input$labelsSizeLogit,
+                        legend_font_size = paste(input$legendFontSizeLogit, "0%", sep = ""), 
+                        legend_width = input$legendWidthLogit,
+                        # dom_id_reset_zoom = "scatterD3-reset-zoomLogit",
+                        # dom_id_svg_export = "scatterD3-svg-exportLogit",
+                        # menu = FALSE,
+                        # height = input$heightVolcanoPlot,
+                        # width=if(input$modifwidthVolcano){input$widthVolcanoPlot},
+                        # disable_wheel = TRUE
+                        )
+      }
+    
+    if(export){
+      data <- data.frame(logit_x, logit_y, color_var)
+      plot <- ggplot(data, aes(x = logit_x, y = logit_y)) + theme_minimal()
+      plot <- plot + geom_point(aes(color = color_var), size = (input$pointSizeLogit %/% 30), alpha = input$pointOpacityLogit)
+      plot <- plot + scale_x_continuous(paste("logit p value", input$Contrast1))
+      plot <- plot + scale_y_continuous(paste("logit p value", input$Contrast2))
+      plot <- plot + theme(axis.title = element_text(size = input$axisFontSizeLogit), 
+                           axis.text = element_text(size = input$axisFontSizeLogit),
+                           legend.title = element_text(size = input$legendFontSizeLogit),
+                           legend.text = element_text(size = input$legendFontSizeLogit))
+      plot <- plot + geom_text(aes(label = labels, color = color_var), size = input$labelsSizeLogit %/% 3, hjust = 1, vjust = 1, show.legend = FALSE)
+      plot <- plot + scale_color_manual(name = "Legend title", values = c("Not significant" = input$colour01, "Significant for contrast 1" = input$colour02,
+                                                                          "Significant for contrast 2" = input$colour03, "Significant for both contrasts" = input$colour04))
+      if (input$fixed11) {
+        plot <- plot + coord_fixed()
+      }
+      if (input$showSignifThresholdsLogitPlot) {
+        plot <- plot + geom_hline(yintercept = alpha_logit, linetype = "dashed", size = input$linesWidthLogitPlot/2)
+        plot <- plot + geom_vline(xintercept = alpha_logit, linetype = "dashed", size = input$linesWidthLogitPlot/2)
+      }
+      if(input$showDiagonal){
+        plot <- plot + geom_abline(slope = 1, intercept = 0, linetype = "dashed", size = input$linesWidthLogitPlot/2)
+      }
+  }
+  return(plot)
+}}
+
 
 ##############################
 ##      VENN DIAGRAM
 ##############################
-Plot_Visu_Venn <- function(input,BaseContrast,resDiff,export=FALSE){
-  
+Plot_Visu_Venn <- function(input,BaseContrast,resDiff, ContrastListVennDebounce, export=FALSE){
   res = NULL
-  SelContrast = input$ContrastList_table_FC
+  #SelContrast = input$ContrastList_table_FCVenn
+  SelContrast = ContrastListVennDebounce()
+  if(length(SelContrast)>=2 & length(SelContrast)<=4){
   data = GetData_venn(input,SelContrast,BaseContrast,resDiff)$res
   res = venn_tooltip(d3vennR(data=data))
-  
+  }
   return(res)
 }
 
+##############################
+##      UPSET
+##############################
+Plot_UpSet <- function(input,BaseContrast, resDiff, ContrastListDebounce, export=FALSE){
+  plot = NULL
+  df = NULL
+  #SelContrast = input$ContrastList_table_FC
+  SelContrast = ContrastListDebounce()
+  if(length(SelContrast)>=2){
+    data = GetData_venn(input,SelContrast,BaseContrast,resDiff)$df.tot
+    listInput <- list()
+    n <- ncol(data)
+    for(i in 1:n){
+      new <- list(na.omit(data[,i]))
+      listInput <- c(listInput, new)
+    }
+    names(listInput) <- colnames(data)
+    
+    # for plot
+    plot <- upset(fromList(listInput), nsets = n, nintersects = NA, set_size.show = input$showNumbers, 
+                  show.numbers = if(input$showNumbers){"yes"}else{"no"}, sets = SelContrast, keep.order = !(input$orderBySize),
+                  point.size = input$pointSizeUpSet, line.size = 1.5, order.by = input$orderByUpset, 
+                  mainbar.y.label = "Number of differential features in common", sets.x.label = "Number of differential features", 
+                  text.scale = c(2, 1.6, 1.6, 1.6, 2, 1.5), decreasing = input$decreasingUpset)
+    
+    # for table
+    combos <- Reduce(c,lapply(rev(1:length(listInput)), function(x) combn(rev(colnames(data)),x,simplify=FALSE) ))
+    
+    listSignif <- Reduce(function(a,b) {unique(c(as.character(a),as.character(b)))}, listInput)
+    n_col = length(combos)
+    n_row = length(listSignif)
+    df <- matrix(rep(listSignif, times = n_col), ncol = n_col)
+    lst <- list()
+    for(j in 1:n_col){for(i in 1:n_row){group_contrasts <- unlist(combos[j])
+                                        if (is.element(df[i,j], lst)){df[i,j] <- NA}
+                                        else{if (!is.element(df[i,j], Reduce(intersect, listInput[group_contrasts]))){df[i,j] <- NA}
+                                             else{lst <- c(lst,df[i,j])}
+                                            }
+                                        }
+                      }
+    colnames(df) <- lapply(combos, function(lstCont) paste(lstCont, collapse = " & "))
+
+    df = as.data.frame(apply(df,2,Go_data_top))
+    maxRow = max(apply(df,2,FUN=function(x) length(which(!is.na(x)))))
+    df = df[1:max(maxRow,1),]
+    df = df[,which(apply(!is.na(df),2,any))]
+  }
+  return(list(plot=plot,table=df))
+}
+
 
+##############################
+##   MULTIPLE VENN COMPAIR
+##############################
+Plot_MultipleVenn <- function(input,BaseContrast, resDiff, ContrastListDebounce){
+  plot = NULL
+  SelContrast = ContrastListDebounce()
+  if(length(SelContrast)>=2){
+  data = GetData_venn(input,SelContrast,BaseContrast,resDiff)$res_multiple_venn
+
+  plot <- ggplot(data,aes(x=x,y=y)) + geom_point() + theme_bw() + 
+          geom_label_repel(aes(label=name), size = input$labelSizemultipleVenn) + xlim(c(0,1)) + ylim(c(0,1)) + 
+          xlab(bquote(Contrast1 *intersect(Contrast2) ~ "/ Contrast1")) + 
+          ylab(bquote(Contrast1 *intersect(Contrast2) ~ "/ Contrast2"))
+  plot <- plot + theme(axis.title = element_text(size = input$FontSizeMultipleVenn), 
+                 axis.text = element_text(size = input$FontSizeMultipleVenn - 2))
+  }
+  return(plot)
+}
 
 
 ##############################################################
@@ -77,6 +318,7 @@ Go_data_top <- function(vect)
 
 
 ## Get the data to plot the heatmap with the FC
+## and for the logit plot
 Get_log2FC_padj <-function(input,BaseContrast,resDiff, info = NULL)
 {
   log2FC = NULL
@@ -100,17 +342,20 @@ Get_log2FC_padj <-function(input,BaseContrast,resDiff, info = NULL)
                                 independentFiltering=input$IndFiltering,alpha=alpha)
     }
     log2FC = as.matrix(round(result[[SelContrast[1]]][, "log2FoldChange"], 3))
-    padj = as.matrix(round(result[[SelContrast[1]]][, "padj"], 3))
+    #padj = as.matrix(round(result[[SelContrast[1]]][, "padj"], 3))
+    padj = as.matrix((result[[SelContrast[1]]][, "padj"]))
     if(nbCont>1)
     {
       for(i in 2:nbCont)
       {
         log2FC = cbind(log2FC,round(result[[SelContrast[i]]][, "log2FoldChange"], 3))
-        padj = cbind(padj,round(result[[SelContrast[i]]][, "padj"], 7))
+        #padj = cbind(padj,round(result[[SelContrast[i]]][, "padj"], 7))
+        padj = cbind(padj,(result[[SelContrast[i]]][, "padj"]))
       }
+    }#  
       colnames(log2FC) = names(result)
       colnames(padj) = names(result)
-    }
+    #}
     
     rownames(log2FC) = rownames(result[[SelContrast[1]]])
     rownames(padj) = rownames(result[[SelContrast[1]]])
@@ -188,6 +433,7 @@ venn_tooltip <- function(venn){
 GetData_venn <-function(input,SelContrast,BaseContrast,resDiff)
 {
   res = list()
+  res_multiple_venn = NULL
   df.tot = NULL
   VarInt = input$VarInt
   dds = resDiff$dds
@@ -208,11 +454,12 @@ GetData_venn <-function(input,SelContrast,BaseContrast,resDiff)
                                 independentFiltering=input$IndFiltering,alpha=alpha)
     }
     padj = round(result[[SelContrast[1]]][, "padj"], 3)
+    
     # save(result,padj,SelContrast,file = "test1.RData")
     df = as.matrix(rownames(result[[1]]))
-    if(length(which(padj>alpha))>0) df[which(padj>alpha),]=NA 
-    if(any(is.na(padj))) df[which(is.na(padj)),]=NA 
-    
+    if(length(which(padj>alpha))>0) df[which(padj>alpha),]=NA
+    if(any(is.na(padj))) df[which(is.na(padj)),]=NA
+
     if(nbCont>1)
     {
       for(i in 2:nbCont)
@@ -226,32 +473,55 @@ GetData_venn <-function(input,SelContrast,BaseContrast,resDiff)
       colnames(df) = SelContrast
       df = as.data.frame(df)
     }
-    
     ## Keep the entire dataframe
     df.tot = as.data.frame(apply(df,2,Go_data_top))
     maxRow = max(apply(as.data.frame(apply(df,2,Go_data_top)),2,FUN=function(x) length(which(!is.na(x)))))
-    
     df.tot = df.tot[1:max(maxRow,1),]
     ## Remove col with only NA
     df = df[,which(apply(!is.na(df),2,any))]
     
     ncont = ncol(as.data.frame(df))
     names.df = names(df)
+    rownames_multiple_venn = c()
     cmp=1
     if(ncont>1 && !is.null(ncont))
     {
       for(i in 1:(ncont))
       {
         for(j in i:ncont)
-        {
-          if(i!=j) res[[cmp]] = list(sets=list(names.df[i],names.df[j]),size= length(which(!is.na(intersect(df[,i],df[,j])))))
-          if(i==j) res[[cmp]] = list(sets=list(names.df[i]),size= length(which(!is.na(intersect(df[,i],df[,i])))))
-          cmp=cmp+1
+          # For Venn diagram (until intersection of 4 contrasts)
+        { if(i==j) {res[[cmp]] = list(sets=list(names.df[i]),size= length(which(!is.na(intersect(df[,i],df[,i])))))
+                    cmp=cmp+1}
+          if(i!=j) {res[[cmp]] = list(sets=list(names.df[i],names.df[j]),size= length(which(!is.na(intersect(df[,i],df[,j])))))
+                    cmp=cmp+1
+                    for(k in j:ncont){
+                      if(k!=j){
+                        res[[cmp]] = list(sets=list(names.df[i],names.df[j],names.df[k]), size = length(which(!is.na(intersect(intersect(df[,i],df[,j]),df[,k])))))
+                        cmp=cmp+1
+                        for(l in k:ncont){
+                          if(l!=k){
+                            res[[cmp]] = list(sets=list(names.df[i],names.df[j],names.df[k],names.df[l]), size = length(which(!is.na(intersect(intersect(intersect(df[,i],df[,j]),df[,k]),df[,l])))))
+                            cmp=cmp+1}
+                        }
+                        }
+                    }
+          }
+          # For 'Multiple Venn Compair'
+          if(i!=j) {
+            if (is.null(res_multiple_venn))
+              {res_multiple_venn <- data.frame(name = c(rownames_multiple_venn, paste(names.df[i],names.df[j], sep = " vs ")), 
+                                                   x = c(length(which(!is.na(intersect(df[,i],df[,j]))))/length(which(!is.na(intersect(df[,i],df[,i]))))), 
+                                                   y = c(length(which(!is.na(intersect(df[,i],df[,j]))))/length(which(!is.na(intersect(df[,j],df[,j]))))),
+                                                   stringsAsFactors=FALSE)}
+            else{new_row <- data.frame(name = c(rownames_multiple_venn, paste(names.df[i],names.df[j], sep = " vs ")),
+                         x = c(length(which(!is.na(intersect(df[,i],df[,j]))))/length(which(!is.na(intersect(df[,i],df[,i]))))),
+                         y = c(length(which(!is.na(intersect(df[,i],df[,j]))))/length(which(!is.na(intersect(df[,j],df[,j]))))))
+                res_multiple_venn <- rbind(res_multiple_venn, new_row)}
+          }
         }
       }
     }
-    
   }
-  return(list(res=res,df.tot=df.tot))
+  return(list(res=res,df.tot=df.tot,res_multiple_venn=res_multiple_venn))
 }
 

Rfunctions/DiagPlot.R

@@ -138,7 +138,7 @@ HCPlot <- function (input,dds,group,type.trans=NULL,counts=NULL,CT,tree,col = c(
       ## Get the type of dendrogram
       type <- input$typeHculst
       
-      dend <- set(dend, "labels_cex", input$cexLabelDiag)
+      #dend <- set(dend, "labels_cex", input$cexLabelDiag)
       if(input$colorHC) labels_colors(dend)<-col[as.integer(as.factor(group))][order.dendrogram(dend)]
       if(type=="hori") 
       { 
@@ -148,7 +148,8 @@ HCPlot <- function (input,dds,group,type.trans=NULL,counts=NULL,CT,tree,col = c(
       else
       {
         par(cex=input$cexTitleDiag,mar=c(6,6,4,5))
-        res = circlize_dendrogram(dend, labels_track_height = 0.2, dend_track_height = .3, main = "Cluster dendrogram",xlab = paste(input$DistClust,"distance, Ward criterion",sep=" "))
+        res = circlize_dendrogram(dend, labels_track_height = 0.2, dend_track_height = .3, main = "Cluster dendrogram",xlab = paste(input$DistClust,"distance, Ward criterion",sep=" "), 
+                                  labels_cex = input$cexLabelDiag)
       }
     }
   }
@@ -554,8 +555,8 @@ PCAPlot_meta <-function(input,dds, group_init, n = min(500, nrow(counts(dds))),
   { 
     type.trans <- type.trans[1]
     
-    if (type.trans == "VST") counts.trans <- assay(varianceStabilizingTransformation(dds))
-    else counts.trans <- assay(rlogTransformation(dds))
+    if (type.trans == "VST") counts.trans <- SummarizedExperiment::assay(varianceStabilizingTransformation(dds))
+    else counts.trans <- SummarizedExperiment::assay(rlogTransformation(dds))
     counts.trans = counts.trans[,ind_kept]
     
     rv = apply(counts.trans, 1, var, na.rm = TRUE)
@@ -754,8 +755,8 @@ Get_pca_table <-function(input,dds, group_init, n = min(500, nrow(counts(dds))),
   { 
     type.trans <- type.trans[1]
     
-    if (type.trans == "VST") counts.trans <- assay(varianceStabilizingTransformation(dds))
-    else counts.trans <- assay(rlogTransformation(dds))
+    if (type.trans == "VST") counts.trans <- SummarizedExperiment::assay(varianceStabilizingTransformation(dds))
+    else counts.trans <- SummarizedExperiment::assay(rlogTransformation(dds))
     counts.trans = counts.trans[,ind_kept]
     
     rv = apply(counts.trans, 1, var, na.rm = TRUE)

Rfunctions/Stat_Model.R

@@ -47,22 +47,22 @@ Get_dds_object <- function(input,counts,target,design,normFactorsOTU,CT_noNorm,C
 #   }
 #   countsNorm = counts(dds, normalized = TRUE)
 
-  if(as.numeric(R.Version()$major)>=3 && as.numeric(R.Version()$minor) >=1.3){
-    if(nrow(counts)*nrow(target)>=50000)
-    {
-      try(DESeq(dds,fitType=input$fitType,parallel = TRUE,minReplicatesForReplace = Inf) ->ddstmp,silent = TRUE)
-      if(!is.null(ddstmp)) dds = ddstmp
-    }
-    if(nrow(counts)*nrow(target)<50000 || is.null(ddstmp))
-    {
-      dds <- estimateDispersions(dds, fitType=input$fitType)
-      dds <- nbinomWaldTest(dds)
-    }
-  }else{
+  # if(as.numeric(R.Version()$major)>=3 && as.numeric(R.Version()$minor) >=1.3){
+  #   if(nrow(counts)*nrow(target)>=50000)
+  #   {
+  #     try(DESeq(dds,fitType=input$fitType,parallel = TRUE,minReplicatesForReplace = Inf) ->ddstmp,silent = TRUE)
+  #     if(!is.null(ddstmp)) dds = ddstmp
+  #   }
+  #   if(nrow(counts)*nrow(target)<50000 || is.null(ddstmp))
+  #   {
+  #     dds <- estimateDispersions(dds, fitType=input$fitType)
+  #     dds <- nbinomWaldTest(dds)
+  #   }
+  # }else{
     dds <- estimateDispersions(dds, fitType=input$fitType)