Filtering step +bugs intervalles confiance alpha diversiy

Rfunctions/Data_Management.R

@@ -195,7 +195,7 @@ GetCountsMerge <- function(input,dataInput,taxoSelect,target,design)
       CT=CT_int
     } else CT = CT[,ind]
     
-    #save.image("test.RData")
+    
     ## Order CT according to the target
     CT = OrderCounts(counts=CT,labels=labels)$CountsOrder
     CT_noNorm = CT
@@ -207,8 +207,9 @@ GetCountsMerge <- function(input,dataInput,taxoSelect,target,design)
     if(is.null(VarNorm)){
       ## Counts normalisation
       ## Normalisation with or without 0
-      if(input$AccountForNA || RowProd==0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT))
-      if(!input$AccountForNA && RowProd!=0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)))
+      if(input$AccountForNA=="NonNull" || RowProd==0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT))
+      if(input$AccountForNA=="All" && RowProd!=0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)))
+      if(input$AccountForNA=="Weighted" && input$AccountForNA!="NonNull" ) {dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT)); sizeFactors(dds) = w.sizefactor(CT)}
       normFactors = sizeFactors(dds)
       
     } else{
@@ -225,13 +226,16 @@ GetCountsMerge <- function(input,dataInput,taxoSelect,target,design)
           CT_tmp = removeNulCounts(CT_tmp) 
           target_tmp = data.frame(labels = rownames(target)[indgrp])
           dds_tmp <- DESeqDataSetFromMatrix(countData=CT_tmp, colData=target_tmp, design=~labels)
-          if(input$AccountForNA) dds_tmp = estimateSizeFactors(dds_tmp,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT_tmp))
-          if(!input$AccountForNA) dds_tmp = estimateSizeFactors(dds_tmp,locfunc=eval(as.name(input$locfunc)))
-          normFactors[indgrp] = sizeFactors(dds_tmp)
+          if(input$AccountForNA=="NonNull") {dds_tmp = estimateSizeFactors(dds_tmp,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT_tmp)); normFactors[indgrp] = sizeFactors(dds_tmp)}
+          if(input$AccountForNA=="All") {dds_tmp = estimateSizeFactors(dds_tmp,locfunc=eval(as.name(input$locfunc))); normFactors[indgrp] = sizeFactors(dds_tmp)}
+          if(input$AccountForNA=="Weighted" && input$AccountForNA!="NonNull" ) {dds_tmp = estimateSizeFactors(dds_tmp,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT_tmp)); normFactors[indgrp] = w.sizefactor(CT_tmp)}
+          
         }
       } else{
-        if(input$AccountForNA || RowProd==0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT))
-        if(!input$AccountForNA && RowProd!=0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)))
+        if(input$AccountForNA=="NonNull" || RowProd==0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT))
+        if(input$AccountForNA=="All" && RowProd!=0) dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)))
+        if(input$AccountForNA=="Weighted" && input$AccountForNA!="NonNull" ) {dds = estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)),geoMeans=GeoMeansCT(CT)); sizeFactors(dds) = w.sizefactor(CT)}
+        
         normFactors = sizeFactors(dds)
       }
       
@@ -240,7 +244,7 @@ GetCountsMerge <- function(input,dataInput,taxoSelect,target,design)
     
     ## Keep normalized OTU table
     CT_Norm = counts(dds, normalized=TRUE)
-    #save(CT_Norm,dds,CT,taxo,taxoSelect,file="test.RData")
+    
     # Only interesting OTU
     # merged_table = merge(CT, taxo[order(rownames(CT)),], by="row.names")
     merged_table = merge(CT, taxo, by="row.names")
@@ -254,7 +258,7 @@ GetCountsMerge <- function(input,dataInput,taxoSelect,target,design)
     #       indOTU_annot = which(rownames(CT)%in%rownames(taxo))
     #       counts_annot = CT[indOTU_annot[ordOTU],]
     ## Aggregate matrix
-    if(taxoSelect=="OTU/Gene") counts = counts_annot
+    if(taxoSelect=="OTU/Gene" || input$FileFormat=="fileBiom") counts = counts_annot
     else{
       if(input$TypeTable == "MGS"){
         taxoS = taxo[,input$TypeTable]
@@ -294,6 +298,62 @@ GeoMeansCT <- function(CT)
   return(gm)
 }
 
+## Get weighted size factors
+w.sizefactor <- function(CT)
+{
+  sf = c()
+  CT = as.matrix(CT)
+  nbsamp = ncol(CT)
+  CT[which(CT<1)]=NA
+  gm = apply(CT,1,geometric.mean,na.rm=TRUE)
+  weights = nbsamp - apply(CT,1,FUN=function(x){tmp =length(which(is.na(x))) ;return(tmp)})
+  weights_tmp = weights
+  
+  for(i in 1:ncol(CT))
+  {
+    ind = which(is.na(CT[,i]))
+    gm_tmp = gm
+    tmp = CT[,i]
+    if(length(ind)>0) {tmp = CT[-ind,i]; gm_tmp = gm[-ind]; weights_tmp = weights[-ind]}
+    sf[i] = w.median(tmp/gm_tmp,weights_tmp, na.rm = TRUE)
+  }
+  names(sf) = colnames(CT)
+  return(sf)
+}
+
+## Calculated the weighted median
+w.median <- function (x, w, na.rm = TRUE) 
+{
+  if (missing(w)) 
+    w <- rep.int(1, length(x))
+  else {
+    if (length(w) != length(x)) 
+      stop("'x' and 'w' must have the same length")
+    if (any(is.na(w))) 
+      stop("NA weights not allowed")
+    if (any(w < 0)) 
+      stop("Negative weights not allowed")
+  }
+  if (is.integer(w)) 
+    w <- as.numeric(w)
+  if (na.rm) {
+    w <- w[i <- !is.na(x)]
+    x <- x[i]
+  }
+  if (all(w == 0)) {
+    warning("All weights are zero")
+    return(NA)
+  }
+  o <- order(x)
+  x <- x[o]
+  w <- w[o]
+  p <- cumsum(w)/sum(w)
+  n <- sum(p < 0.5)
+  if (p[n + 1] > 0.5) 
+    x[n + 1]
+  else (x[n + 1] + x[n + 2])/2
+}
+
 
 ## Order the counts 
 OrderCounts <- function(counts,normFactors=NULL,labels)
@@ -311,3 +371,171 @@ OrderCounts <- function(counts,normFactors=NULL,labels)
   return(list(CountsOrder=CountsOrder,normFactorsOrder = normFactorsOrder))
 }
 
+
+## Order the counts 
+Filtered_feature <- function(counts,th.samp,th.abund)
+{
+  ind = NULL
+  
+  ## Total abundance over samples
+  Tot_abundance = log(rowSums(counts)+1)
+  ind.ab = which(Tot_abundance<=th.abund)
+  
+  ## Get the numbre of non zero sample
+  counts.bin = as.matrix(counts)
+  counts.bin[which(counts>0)] = 1
+  nbSampByFeat = rowSums(counts.bin)
+  
+  ind.samp = which(nbSampByFeat<=th.samp)
+  
+  ind = unique(c(ind.samp,ind.ab))
+  
+  return(list(ind=ind,Tot_abundance=Tot_abundance,ind.ab=ind.ab,counts.bin=counts.bin,ind.samp=ind.samp,nbSampByFeat=nbSampByFeat))
+}
+
+
+
+## Order the counts 
+plot_filter <- function(counts,th.samp,th.abund,type="Scatter")
+{
+  res = NULL
+  
+  ## Initial plot for plotly
+  if(type == 'Abundance' || type == 'Samples'){ 
+    dataNull = data.frame(x=c(0,0),y=c(1,2),col=c("white","white"))
+    res = ggplotly(ggplot(dataNull,aes(x=x,y=y))+geom_point(aes(colour = col))+theme_bw()+ scale_color_manual(values = "white"))
+  }
+  
+  res_filter = Filtered_feature(counts,th.samp,th.abund)
+  if(type == 'Abundance' && !is.null(th.samp) && !is.null(th.abund) )
+  {
+    state = rep("Kept",nrow(counts))
+    ## Total abundance over samples
+    Tot_abundance = res_filter$Tot_abundance
+    
+    ind = res_filter$ind.ab
+    ord = order(Tot_abundance,decreasing = FALSE)
+    
+    ## Modify the state
+    state[ind] = "Removed"
+    
+    ## Create the data.frame for ggplot
+    df = data.frame(lab = rownames(counts)[ord],y = Tot_abundance[ord],State=state[ord])
+    df$lab = factor(df$lab,levels = rownames(counts)[ord])
+    df$State = factor(df$State,levels = c("Kept","Removed"))
+    
+    ## plot the results
+    gg = ggplot(df,aes(lab,y,fill=State)) + geom_bar(stat='identity') + theme_bw() +theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust = 0.5))
+    gg = gg + geom_hline( yintercept = th.abund,linetype = "longdash")
+    gg = gg + scale_fill_manual(values = c('springgreen3','firebrick'))
+    if(!"Kept"%in%df$State ) gg = gg + scale_fill_manual(values = 'firebrick')
+    if(!"Removed"%in%df$State ) gg = gg + scale_fill_manual(values = 'springgreen3')
+    
+    res = ggplotly(gg)
+  }
+  
+  if(type == 'Samples' && !is.null(th.samp) && !is.null(th.abund))
+  {
+    state = rep("Kept",nrow(counts))
+    
+    ## Get the number of non zero sample
+    nbSampByFeat = res_filter$nbSampByFeat
+    ind = res_filter$ind.samp
+    ord = order(nbSampByFeat,decreasing = FALSE)
+    
+    state[ind] = "Removed"
+    
+    df = data.frame(lab = rownames(counts)[ord],y = nbSampByFeat[ord],State=state[ord])
+    df$lab = factor(df$lab,levels = rownames(counts)[ord])
+    df$State = factor(df$State,levels = c("Kept","Removed"))
+    ## plot the results
+    
+    gg = ggplot(df,aes(lab,y,fill=State)) + geom_bar(stat='identity') + theme_bw() +theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust = 0.5))
+    gg = gg + geom_hline( yintercept = th.samp,linetype = "longdash")
+    gg = gg + scale_fill_manual(values = c('springgreen3','firebrick'))  
+    if(!"Kept"%in%df$State ) gg = gg + scale_fill_manual(values = 'firebrick')
+    if(!"Removed"%in%df$State ) gg = gg + scale_fill_manual(values = 'springgreen3')
+    
+    res = ggplotly(gg)
+    
+  }
+  
+  if(type == 'Scatter')
+  {
+    state = rep("Kept",nrow(counts))
+    
+    ## Get the number of non zero sample
+    nbSampByFeat = res_filter$nbSampByFeat
+    Tot_abundance = res_filter$Tot_abundance
+    
+    ## Get the selected features (under the thresholds)
+    ind = res_filter$ind
+    
+    ## Modify the state
+    state[ind] = "Removed"
+    
+    ## Create the data.frame for ggplot
+    df = data.frame(lab =rownames(counts), y = nbSampByFeat,x = Tot_abundance,State=state)
+    df$lab = factor(df$lab,levels = rownames(counts))
+    df$State = factor(df$State,levels = c("Kept","Removed"))
+    
+    x_var = df$x
+    y_var = df$y
+    State = df$State
+    PointSize = 2
+    colors_scat = list(Kept="#00CD66",Removed="#b22222")
+    print(State)
+    print(colors_scat)
+    print(head(df))
+    res = scatterD3(x = x_var,
+                     y = y_var,
+                     lab = rownames(df),
+                     xlab = "Abundance in log",
+                     ylab = "Number of samples",
+                     col_var = State,
+                     colors = colors_scat,
+                     size_lab = PointSize,
+                     key_var = rownames(df),
+                     point_opacity = 0.7,
+                     transitions = TRUE)
+    
+    
+#     gg = ggplot(df,aes(x,y,color=State,group = lab)) + geom_point() + theme_bw()
+#     gg = gg + geom_vline( xintercept = th.samp,linetype = "longdash")
+#     gg = gg + geom_hline( yintercept = th.abund,linetype = "longdash")
+#     gg = gg + scale_color_manual(values = c('springgreen3','firebrick'))
+#     ggplotly(gg)
+#     return(gg)
+  }
+  
+  return(res)
+}
+
+
+
+######################################################
+## NAME: SelectThreshAb 
+##
+## INPUT:
+##    infile : data matrix (counts, rows: taxo)
+##    lambda : Tuning parameter (default is 500)
+##
+## OUTPUT:
+##    Cut-off value 
+##
+######################################################
+
+SelectThreshAb <- function(infile,lambda=500,graph=TRUE){
+  
+  rs <- rowSums(infile)
+  test_Filtre <- sapply(c(min(rs):lambda),FUN=function(x) table(rs>x))
+  x <- c(min(rs):lambda)
+  reslm <- lm(test_Filtre[1,]~x)$coefficients
+  val <- which(test_Filtre[1,]>reslm[1])[1]
+  if (graph==TRUE){
+    plot(test_Filtre[1,],ylab="Nb especes avec moins de x comptages sur tous les echantillons",xlab="x")
+    abline(v=val,col="red")
+  }
+  return(val)
+}
+

Rfunctions/VisuPlot.R

@@ -269,7 +269,7 @@ Plot_Visu_Scatterplot<- function(input,resDiff,export=FALSE,lmEst = FALSE,CorEst
       size_var = if (PointSize == "None" || is.null(PointSize))  1 else data[,PointSize]
       
       plot = ggplot(data, aes(x = x_var, y = y_var)) + geom_point(aes(color=col_var,size =size_var,shape = symbol_var),alpha=0.7) +theme_bw()
-      plot = plot + geom_smooth(method="lm")
+      if(input$AddRegScatter) plot = plot + geom_smooth(method="lm")
       if(input$SizeLabelScatter!=0) plot = plot + geom_text(aes(label=rownames(data),color=col_var,size=as.numeric(input$SizeLabelScatter)/10),vjust = 0,nudge_y =0.05)
       plot = plot + xlab(Xvar) + ylab(Yvar)
       
@@ -333,10 +333,10 @@ Plot_Visu_Diversity <- function(input,resDiff,ForScatter=FALSE){
   if(nrow(counts_tmp_combined)>0 && !is.null(counts_tmp_combined) && !is.null(targetInt))
   { 
     sqrt.nb = sqrt(table(targetInt$AllVar))
-    
+    # save(counts_tmp_combined,targetInt,file = "testDiv.RData")
     alpha <- tapply(TaxoNumber(counts_tmp_combined), targetInt$AllVar, mean)
-    ci.alpha.down = pmax(alpha - 1.96*tapply(TaxoNumber(counts_tmp_combined), targetInt$AllVar, mean)/sqrt.nb,0)
-    ci.alpha.up = alpha + 1.96*tapply(TaxoNumber(counts_tmp_combined), targetInt$AllVar, mean)/sqrt.nb
+    ci.alpha.down = pmax(alpha - 1.96*tapply(TaxoNumber(counts_tmp_combined), targetInt$AllVar, sd)/sqrt.nb,0)
+    ci.alpha.up = alpha + 1.96*tapply(TaxoNumber(counts_tmp_combined), targetInt$AllVar, sd)/sqrt.nb
     
     shan <- tapply(diversity(counts_tmp_combined, index = "shannon"), targetInt$AllVar, mean)
     ci.shan.down = pmax(shan - 1.96*tapply(diversity(counts_tmp_combined, index = "shannon"), targetInt$AllVar, sd)/sqrt.nb,0)
@@ -413,7 +413,7 @@ Plot_Visu_Diversity <- function(input,resDiff,ForScatter=FALSE){
     ## Get interactivity
     #ff = ggplotly(gg)
   }
-  print(dataTmp)
+  
   return(list(plot=gg,dataDiv = dataTmp))
   
 }

server.R

 source('LoadPackages.R')
+library(plotly)
 
 shinyServer(function(input, output,session) {
 
@@ -166,7 +167,7 @@ shinyServer(function(input, output,session) {
   
   
   ## Merge counts data
-  dataMergeCounts <-reactive({ 
+  dataMergeCounts <-reactive({
     input$RunDESeq
     
     counts = NULL
@@ -175,15 +176,24 @@ shinyServer(function(input, output,session) {
     CT_noNorm = NULL
     CT_Norm = NULL
     #labeled= NULL
-    data = dataInput()$data
+    data = isolate(dataInput()$data)
     target = isolate(dataInputTarget()$target)
     taxo = isolate(input$TaxoSelect)
     
+    withProgress(
     if(!is.null(data$counts) && !is.null(data$taxo) && nrow(data$counts)>0 && nrow(data$taxo)>0 && !is.null(taxo) && taxo!="..." && !is.null(target)) 
     {
-      design = GetDesign(input)
-      tmp = isolate(withProgress(GetCountsMerge(input,data,taxo,target,design),message="Merging the counts ..."))
+      design = GetDesign(isolate(input))
+      tmp = isolate(GetCountsMerge(input,data,taxo,target,design))
       counts = tmp$counts
+      
+      ## Filtering the counts
+      if(isolate(input$AddFilter) && !is.null(isolate(input$SliderThSamp)) && !is.null(isolate(input$SliderThAb)))
+      {
+        ind.filter =Filtered_feature(counts,isolate(input$SliderThSamp),isolate(input$SliderThAb))$ind
+        counts = counts[-ind.filter,]
+      }
+      
       CheckTarget = tmp$CheckTarget
       #target = tmp$target
       #labeled = tmp$labeled
@@ -193,6 +203,7 @@ shinyServer(function(input, output,session) {
       CT_noNorm = tmp$CT_noNorm
       CT_Norm = tmp$CT_Norm
     }
+    ,message="Merging the counts ...")
     return(list(counts=counts,CheckTarget=CheckTarget,normFactors=normFactors,CT_noNorm=CT_noNorm, CT_Norm=CT_Norm))
     #return(list(counts=counts,target=target,labeled=labeled,normFactors=normFactors,CT_noNorm=CT_noNorm))
   })
@@ -258,7 +269,60 @@ shinyServer(function(input, output,session) {
   })
   
   
+  ####### Filtering the counts (sliders)
+  
+  output$ThAb <- renderUI({
+    input$AddFilter
+    
+    res = NULL
+    counts = isolate(dataMergeCounts()$counts)
+    tot = rowSums(counts)
+    tmp = SelectThreshAb(counts,lambda=max(round(sum(counts)/nrow(counts)*0.05),min(tot)+1),graph=FALSE)
+    
+    res = sliderInput("SliderThAb","Threshold on the total abundance (in log)",min=0,max=round(max(log(tot+1)),1),value = log(tmp+1))
+    return(res)
+  })
+  
+  
+  output$ThSamp <- renderUI({
+    input$AddFilter
+    
+    res = NULL
+    counts = isolate(dataMergeCounts()$counts)
+    counts.bin = as.matrix(counts)
+    counts.bin[which(counts>0)] = 1
+    nbSampByFeat = rowSums(counts.bin)
+    
+    ## Default value
+    val = round(max(nbSampByFeat)*0.2)
+    
+    res = sliderInput("SliderThSamp","Threshold on the minimal number of samples",min=0,max=max(nbSampByFeat),value = val)
+    return(res)
+  })
+  
+  
+  ## Plot for the filtering step$
+  
+  # plot_filter(counts,th.samp,th.abund,type="Scatter")
+  
+  output$Plot_ThAb <- renderPlotly({
+    counts = dataMergeCounts()$counts
+    ## output of plot_filter is ggplot class
+    plot_filter(counts,input$SliderThSamp,input$SliderThAb,type="Abundance")
+    
+  })
+    
+  output$Plot_ThSamp <- renderPlotly({
+    counts = dataMergeCounts()$counts
+    ## output of plot_filter is ggplot class
+    plot_filter(counts,input$SliderThSamp,input$SliderThAb,type="Samples")
+  })
   
+  output$Plot_Scatter_Filter <- renderScatterD3({
+    counts = dataMergeCounts()$counts
+    ## output of plot_filter is ggplot class
+    plot_filter(counts,input$SliderThSamp,input$SliderThAb,type="Scatter")
+  })
   #####################################################
   ##
   ##                DYNAMIC MENU
@@ -545,8 +609,8 @@ shinyServer(function(input, output,session) {
   
   ## Box for merged counts
   output$BoxCountsMerge <- renderUI({
-    
-    counts = dataMergeCounts()$counts
+    input$RunDESeq
+    counts = isolate(dataMergeCounts()$counts)
     taxo = input$TaxoSelect
     
     if(!is.null(counts) && taxo != "...")
@@ -1047,13 +1111,13 @@ shinyServer(function(input, output,session) {
   
   # Infobox taxo
   output$InfoTaxo <- renderInfoBox({
+    input$RunDESeq
+    data = isolate(dataInput()$data)
+    taxo = isolate(input$TaxoSelect)
     
-    data = dataInput()$data
-    taxo = input$TaxoSelect
-    
-    if(!is.null(data$counts) && !is.null(data$taxo) && nrow(data$counts)>0 && nrow(data$taxo)>0 && !is.null(taxo) && taxo!="...") 
+    if(!is.null(data$counts) && !is.null(data$taxo) && nrow(data$counts)>0 && nrow(data$taxo)>0 && !is.null(isolate(taxo)) && taxo!="...") 
     {
-      counts = dataMergeCounts()$counts
+      counts = isolate(dataMergeCounts()$counts)
       nfeature = nrow(counts)
       infoBox(h6(strong("Taxonomy")), subtitle = h6(paste(taxo, ", nb features: ",nfeature,sep="")), icon = icon("thumbs-o-up"),color = "green",width=NULL,fill=TRUE)
     }
@@ -1433,13 +1497,11 @@ shinyServer(function(input, output,session) {
     target = dataInputTarget()$target
     taxo = input$TaxoSelect
     if(!is.null(target) && taxo!="...") res = actionButton("RunDESeq",strong("Run analysis"),icon = icon("caret-right"))
-    
     return(res)
   })
   
   
   
-  
   #####################################################
   ##
   ##                VISUALISATION
@@ -1898,5 +1960,55 @@ shinyServer(function(input, output,session) {
       
       data.frame(refs)
     }
-  }, sanitize.text.function = function(x) x)  
+  }, sanitize.text.function = function(x) x)
+  
+  
+  #####################################################
+  ##
+  ##      Disable/Enable actions
+  ##
+  #####################################################
+  
+  
+  ## Disable the actionbutton if the number of feature is lower than 2
+  observe({
+  
+    counts = dataMergeCounts()$counts
+    if(input$AddFilter && !is.null(input$SliderThSamp) && !is.null(input$SliderThAb))
+    {
+      ind.filter =Filtered_feature(counts,input$SliderThSamp,input$SliderThAb)$ind
+      counts = counts[-ind.filter,]
+    }
+
+    if (!is.null(counts)){
+      if (nrow(counts)>=2){
+        shinyjs::enable("RunDESeq")
+      }
+      if (nrow(counts)<2) {
+        shinyjs::disable("RunDESeq")
+      }
+    } 
+    if (is.null(counts)) {
+      shinyjs::disable("RunDESeq")
+    }
+  })
+  
+  
+  observe({
+    taxo = input$TaxoSelect
+    target=dataInputTarget()$target
+    
+    if(is.null(target) || taxo =="...") 
+    {
+      shinyjs::disable("AddFilter")
+    } else {
+      shinyjs::enable("AddFilter")
+    }
+  })
+  
+  
+
+  
 })
+
+

ui.R

@@ -13,14 +13,14 @@ if(!require(shinyjs)){
   install.packages("shinyjs")
   library(shinyjs)  
 }
-
+library(plotly)
 function(request) {
 sidebar <- dashboardSidebar(
   useShinyjs(),
   inlineCSS(appCSS),
-  tags$head(
-    tags$script(src = "custom.js")
-  ),
+#   tags$head(
+#     tags$script(src = "custom.js")
+#   ),
   div(id = "loading-content-bar",
       p()),
   div(
@@ -64,7 +64,7 @@ body <- dashboardBody(
                               ". SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2", a("[Love 2014]", href="http://www.ncbi.nlm.nih.gov/pubmed/25516281"),".
                               Resulting p-values are adjusted according to the Benjamini and Hochberg procedure [Benjamini and Hochberg 1995].
                               The PCOA is performed with the", a("ade4 R package",href="http://pbil.univ-lyon1.fr/ade4/"), "and plots are generated with", a("ggplot2",href="http://ggplot2.org/"), "or", a("D3.js packages",href="http://d3js.org/"), ".
-                              A presentation about SHAMAN is available", a("here.",target="_blank",href="shaman_presentation.pdf"), br(),
+                              A presentation about SHAMAN is available", a("here",target="_blank",href="shaman_presentation.pdf")," and a poster", a("here.",target="_blank",href="shaman_poster.pdf"), br(),
                               "SHAMAN is compatible with standard formats for metagenomic analysis. We also provide a complete pipeline for OTU picking and annotation named",a("MASQUE", href="https://github.com/aghozlane/masque") ,"used in production at Institut Pasteur.",style = "font-family: 'times'; font-si16pt"),
                             p("Hereafter is the global workflow of the SHAMAN application:"),
                             div(img(src = "Workflow.png",width = "100%",style="max-width: 600px"),Align="center")
@@ -90,6 +90,8 @@ body <- dashboardBody(
             box(
               title = "What's new in SHAMAN", width = NULL, status = "primary",
               div(style = 'overflow-y: scroll; max-height: 400px',
+                  addNews("Oct 12th 2016","Filtering step and bugs fix","You can now apply a filter on the features according to their abundance 
+                          and the number of samples. Bugs on confidence intervals for the alpha diversity have been fixed"),
                   addNews("Sep 21th 2016","SHAMAN on docker","The install of SHAMAN is now available with docker.
                            The R install is also updated and passed in release candidate state."),
                   addNews("Sep 14th 2016","Download and install SHAMAN","You can install SHAMAN (beta)."),
@@ -305,47 +307,73 @@ body <- dashboardBody(
        
               column(width=7,
                 box(title="Options",width = NULL, status = "primary", solidHeader = TRUE,collapsible = TRUE,collapsed = TRUE,
-                    p(strong("Linear model options"),Align ="center"),
-                    hr(),
-                  fluidRow(
-                    column(width=3,
-                      radioButtons("TransType",h6(strong("Type of transformation")),choices = c("VST"="VST","rlog"="rlog"))
-                    ), 
-                    column(width=3,
-                      radioButtons("IndFiltering",h6(s